Methods and systems for speech adaptation data

ABSTRACT

Computationally implemented methods and systems include detecting speech data related to a speech-facilitated transaction, acquiring adaptation data that is at least partly based on at least one speech interaction of a particular party that is discrete from the detected speech data, wherein at least a portion of the adaptation data has been stored on a particular device associated with the particular party, obtaining a destination of one or more of the adaptation data and the speech data, and transmitting one or more of the speech data and the adaptation data to the acquired destination. In addition to the foregoing, other aspects are described in the claims, drawings, and text.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is related to and claims the benefit of theearliest available effective filing date(s) from the following listedapplication(s) (the “Related Applications”) (e.g., claims earliestavailable priority dates for other than provisional patent applicationsor claims benefits under 35 USC §119(e) for provisional patentapplications, for any and all parent, grandparent, great-grandparent,etc. applications of the Related Application(s)). All subject matter ofthe Related Applications and of any and all parent, grandparent,great-grandparent, etc. applications of the Related Applications,including any priority claims, is incorporated herein by reference tothe extent such subject matter is not inconsistent herewith.

RELATED APPLICATIONS

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 13/485,733, entitled SPEECH RECOGNITION ADAPTATIONSYSTEMS BASED ON ADAPTATION DATA, naming Royce A. Levien, Richard T.Lord, Robert W. Lord, Mark A. Malamud, and John D. Rinaldo, Jr. asinventors, filed 31 May 2012, which is currently co-pending or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 13/485,738, entitled SPEECH RECOGNITION ADAPTATIONSYSTEMS BASED ON ADAPTATION DATA, naming Royce A. Levien, Richard T.Lord, Robert W. Lord, Mark A. Malamud, and John D. Rinaldo, Jr. asinventors, filed 31 May 2012, which is currently co-pending or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 13/538,855, entitled SPEECH RECOGNITION ADAPTATIONSYSTEMS BASED ON ADAPTATION DATA, naming Royce A. Levien, Richard T.Lord, Robert W. Lord, Mark A. Malamud, and John D. Rinaldo, Jr. asinventors, filed 29 Jun. 2012, which is currently co-pending or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 13/538,866, entitled SPEECH RECOGNITION ADAPTATIONSYSTEMS BASED ON ADAPTATION DATA, naming Royce A. Levien, Richard T.Lord, Robert W. Lord, Mark A. Malamud, and John D. Rinaldo, Jr. asinventors, filed 29 Jun. 2012, which is currently co-pending or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 13/564,647, entitled SPEECH RECOGNITION ADAPTATIONSYSTEMS BASED ON ADAPTATION DATA, naming Royce A. Levien, Richard T.Lord, Robert W. Lord, Mark A. Malamud, and John D. Rinaldo, Jr. asinventors, filed 1 Aug. 2012, which is currently co-pending or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 13/564,649, entitled SPEECH RECOGNITION ADAPTATIONSYSTEMS BASED ON ADAPTATION DATA, naming Royce A. Levien, Richard T.Lord, Robert W. Lord, Mark A. Malamud, and John D. Rinaldo, Jr. asinventors, filed 1 Aug. 2012, which is currently co-pending or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 13/564,650, entitled SPEECH RECOGNITION ADAPTATIONSYSTEMS BASED ON ADAPTATION DATA, naming Royce A. Levien, Richard T.Lord, Robert W. Lord, Mark A. Malamud, and John D. Rinaldo, Jr. asinventors, filed 1 Aug. 2012, which is currently co-pending or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 13/564,651, entitled SPEECH RECOGNITION ADAPTATIONSYSTEMS BASED ON ADAPTATION DATA, naming Royce A. Levien, Richard T.Lord, Robert W. Lord, Mark A. Malamud, and John D. Rinaldo, Jr. asinventors, filed 1 Aug. 2012, which is currently co-pending or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. To Be Assigned, entitled METHODS AND SYSTEMS FORSPEECH ADAPTATION DATA, naming Royce A. Levien, Richard T. Lord, RobertW. Lord, Mark A. Malamud, and John D. Rinaldo, Jr. as inventors, filed10 Sep. 2012, which is currently co-pending or is an application ofwhich a currently co-pending application is entitled to the benefit ofthe filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. To Be Assigned, entitled METHODS AND SYSTEMS FORSPEECH ADAPTATION DATA, naming Royce A. Levien, Richard T. Lord, RobertW. Lord, Mark A. Malamud, and John D. Rinaldo, Jr. as inventors, filed10 Sep. 2012, which is currently co-pending or is an application ofwhich a currently co-pending application is entitled to the benefit ofthe filing date.

The United States Patent Office (USPTO) has published a notice to theeffect that the USPTO's computer programs require that patent applicantsreference both a serial number and indicate whether an application is acontinuation, continuation-in-part, or divisional of a parentapplication. Stephen G. Kunin, Benefit of Prior-Filed Application, USPTOOfficial Gazette Mar. 18, 2003. The present Applicant Entity(hereinafter “Applicant”) has provided above a specific reference to theapplication(s) from which priority is being claimed as recited bystatute. Applicant understands that the statute is unambiguous in itsspecific reference language and does not require either a serial numberor any characterization, such as “continuation” or“continuation-in-part,” for claiming priority to U.S. patentapplications. Notwithstanding the foregoing, Applicant understands thatthe USPTO's computer programs have certain data entry requirements, andhence Applicant has provided designation(s) of a relationship betweenthe present application and its parent application(s) as set forthabove, but expressly points out that such designation(s) are not to beconstrued in any way as any type of commentary and/or admission as towhether or not the present application contains any new matter inaddition to the matter of its parent application(s).

BACKGROUND

This application is related to adaptation data related to speechprocessing.

SUMMARY

In one or more various aspects, a method includes but is not limited todetecting speech data related to a speech-facilitated transaction,acquiring adaptation data that is at least partly based on at least onespeech interaction of a particular party that is discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on a particular device associated with the particularparty, obtaining a destination of one or more of the adaptation data andthe speech data, and transmitting one or more of the speech data and theadaptation data to the acquired destination. In addition to theforegoing, other method aspects are described in the claims, drawings,and text forming a part of the disclosure set forth herein.

In one or more various aspects, one or more related systems may beimplemented in machines, compositions of matter, or manufactures ofsystems, limited to patentable subject matter under 35 U.S.C. 101. Theone or more related systems may include, but are not limited to,circuitry and/or programming for effecting the herein-referenced methodaspects. The circuitry and/or programming may be virtually anycombination of hardware, software, and/or firmware configured to effectthe herein-referenced method aspects depending upon the design choicesof the system designer, and limited to patentable subject matter under35 USC 101.

In one or more various aspects, a system includes, but is not limitedto, means for detecting speech data related to a speech-facilitatedtransaction, means for acquiring adaptation data that is at least partlybased on at least one speech interaction of a particular party that isdiscrete from the detected speech data, wherein at least a portion ofthe adaptation data has been stored on a particular device associatedwith the particular party, means for obtaining a destination of one ormore of the adaptation data and the speech data, and means fortransmitting one or more of the speech data and the adaptation data tothe acquired destination. In addition to the foregoing, other systemaspects are described in the claims, drawings, and text forming a partof the disclosure set forth herein.

In one or more various aspects, a system includes, but is not limitedto, circuitry for detecting speech data related to a speech-facilitatedtransaction, circuitry for acquiring adaptation data that is at leastpartly based on at least one speech interaction of a particular partythat is discrete from the detected speech data, wherein at least aportion of the adaptation data has been stored on a particular deviceassociated with the particular party, circuitry for obtaining adestination of one or more of the adaptation data and the speech data,and circuitry for transmitting one or more of the speech data and theadaptation data to the acquired destination. In addition to theforegoing, other system aspects are described in the claims, drawings,and text forming a part of the disclosure set forth herein.

In one or more various aspects, a computer program product, comprising asignal bearing medium, bearing one or more instructions including, butnot limited to, one or more instructions for detecting speech datarelated to a speech-facilitated transaction, one or more instructionsfor acquiring adaptation data that is at least partly based on at leastone speech interaction of a particular party that is discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on a particular device associated with the particularparty, one or more instructions for obtaining a destination of one ormore of the adaptation data and the speech data, and one or moreinstructions for transmitting one or more of the speech data and theadaptation data to the acquired destination. In addition to theforegoing, other computer program product aspects are described in theclaims, drawings, and text forming a part of the disclosure set forthherein.

In one or more various aspects, a device is defined by a computationallanguage, such that the device comprises one or more interchainedphysical machines ordered for detecting speech data related to aspeech-facilitated transaction, one or more interchained physicalmachines ordered for acquiring adaptation data that is at least partlybased on at least one speech interaction of a particular party that isdiscrete from the detected speech data, wherein at least a portion ofthe adaptation data has been stored on a particular device associatedwith the particular party, one or more interchained physical machinesordered for obtaining a destination of one or more of the adaptationdata and the speech data, one or more interchained physical machinesordered for transmitting one or more of the speech data and theadaptation data to the acquired destination.

In addition to the foregoing, various other method and/or system and/orprogram product aspects are set forth and described in the teachingssuch as text (e.g., claims and/or detailed description) and/or drawingsof the present disclosure.

The foregoing is a summary and thus may contain simplifications,generalizations, inclusions, and/or omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is NOT intended to be in any way limiting. Otheraspects, features, and advantages of the devices and/or processes and/orother subject matter described herein will become apparent by referenceto the detailed description, the corresponding drawings, and/or in theteachings set forth herein.

BRIEF DESCRIPTION OF THE FIGURES

For a more complete understanding of embodiments, reference now is madeto the following descriptions taken in connection with the accompanyingdrawings. The use of the same symbols in different drawings typicallyindicates similar or identical items, unless context dictates otherwise.The illustrative embodiments described in the detailed description,drawings, and claims are not meant to be limiting. Other embodiments maybe utilized, and other changes may be made, without departing from thespirit or scope of the subject matter presented here.

FIG. 1A shows a high-level block diagram of an exemplary environment100, according to an embodiment.

FIG. 1B shows a high-level block diagram of a personal device 120operating in an exemplary embodiment 100, according to an embodiment.

FIG. 1C shows a high-level diagram of an exemplary environment 100′,which is an example of an exemplary embodiment 100 having a personaldevice 120, according to an embodiment.

FIG. 1D shows a high-level diagram of an exemplary environment 100″,which is an example of an exemplary embodiment 100 having a personaldevice 120, according to an embodiment.

FIG. 1E shows a high-level diagram of an exemplary environment 100′″,which is an example of an exemplary embodiment 100 having a personaldevice 120, according to an embodiment.

FIG. 2, including FIGS. 2A-2D, shows a particular perspective of thespeech data related to speech facilitated transaction detecting module152 of the personal device 120 of environment 100 of FIG. 1B.

FIG. 3, including FIGS. 3A-3K, shows adaptation data at least partlybased on discrete speech interaction of particular party separate fromdetected speech data, and has been stored on a particularparty-associated particular device acquiring module 154 of the personaldevice 120 of environment 100 of FIG. 1B.

FIG. 4, including FIGS. 4A-4D, shows a particular perspective ofdestination of one or more of the adaptation data and the speech dataacquiring module 156 of the personal device 120 of environment 100 ofFIG. 1B.

FIG. 5, including FIGS. 5A-5B, shows a particular perspective of theacquired destination of one or more of the adaptation data and thespeech data transmitting module of the personal device 120 ofenvironment 100 of FIG. 1B.

FIG. 6 is a high-level logic flow chart of a process, e.g., operationalflow 600, according to an embodiment.

FIG. 7A is a high-level logic flowchart of a process depicting alternateimplementations of a detecting speech data operation 602 of FIG. 6,according to one or more embodiments.

FIG. 7B is a high-level logic flowchart of a process depicting alternateimplementations of a detecting speech data operation 602 of FIG. 6,according to one or more embodiments.

FIG. 7C is a high-level logic flowchart of a process depicting alternateimplementations of a detecting speech data operation 602 of FIG. 6,according to one or more embodiments.

FIG. 7D is a high-level logic flowchart of a process depicting alternateimplementations of a detecting speech data operation 602 of FIG. 6,according to one or more embodiments.

FIG. 8A is a high-level logic flowchart of a process depicting alternateimplementations of an acquiring adaptation data operation 604 of FIG. 6,according to one or more embodiments.

FIG. 8B is a high-level logic flowchart of a process depicting alternateimplementations of an acquiring adaptation data operation 604 of FIG. 6,according to one or more embodiments.

FIG. 8C is a high-level logic flowchart of a process depicting alternateimplementations of an acquiring adaptation data operation 604 of FIG. 6,according to one or more embodiments.

FIG. 8D is a high-level logic flowchart of a process depicting alternateimplementations of an acquiring adaptation data operation 604 of FIG. 6,according to one or more embodiments.

FIG. 8E is a high-level logic flowchart of a process depicting alternateimplementations of an acquiring adaptation data operation 604 of FIG. 6,according to one or more embodiments.

FIG. 8F is a high-level logic flowchart of a process depicting alternateimplementations of an acquiring adaptation data operation 604 of FIG. 6,according to one or more embodiments.

FIG. 8G is a high-level logic flowchart of a process depicting alternateimplementations of an acquiring adaptation data operation 604 of FIG. 6,according to one or more embodiments.

FIG. 8H is a high-level logic flowchart of a process depicting alternateimplementations of an acquiring adaptation data operation 604 of FIG. 6,according to one or more embodiments.

FIG. 8I is a high-level logic flowchart of a process depicting alternateimplementations of an acquiring adaptation data operation 604 of FIG. 6,according to one or more embodiments.

FIG. 8J is a high-level logic flowchart of a process depicting alternateimplementations of an acquiring adaptation data operation 604 of FIG. 6,according to one or more embodiments.

FIG. 8K is a high-level logic flowchart of a process depicting alternateimplementations of an acquiring adaptation data operation 604 of FIG. 6,according to one or more embodiments.

FIG. 8L is a high-level logic flowchart of a process depicting alternateimplementations of an acquiring adaptation data operation 604 of FIG. 6,according to one or more embodiments.

FIG. 8M is a high-level logic flowchart of a process depicting alternateimplementations of an acquiring adaptation data operation 604 of FIG. 6,according to one or more embodiments.

FIG. 8N is a high-level logic flowchart of a process depicting alternateimplementations of an acquiring adaptation data operation 604 of FIG. 6,according to one or more embodiments.

FIG. 8P is a high-level logic flowchart of a process depicting alternateimplementations of an acquiring adaptation data operation 604 of FIG. 6,according to one or more embodiments.

FIG. 8Q is a high-level logic flowchart of a process depicting alternateimplementations of an acquiring adaptation data operation 604 of FIG. 6,according to one or more embodiments.

FIG. 9A is a high-level logic flowchart of a process depicting alternateimplementations of an obtaining a destination operation 606 of FIG. 6,according to one or more embodiments.

FIG. 9B is a high-level logic flowchart of a process depicting alternateimplementations of an obtaining a destination operation 606 of FIG. 6,according to one or more embodiments.

FIG. 9C is a high-level logic flowchart of a process depicting alternateimplementations of an obtaining a destination operation 606 of FIG. 6,according to one or more embodiments.

FIG. 9D is a high-level logic flowchart of a process depicting alternateimplementations of an obtaining a destination operation 606 of FIG. 6,according to one or more embodiments.

FIG. 10A is a high-level logic flowchart of a process depictingalternate implementations of a transmitting operation 608 of FIG. 6,according to one or more embodiments.

FIG. 10B is a high-level logic flowchart of a process depictingalternate implementations of a transmitting operation 608 of FIG. 6,according to one or more embodiments.

FIG. 10C is a high-level logic flowchart of a process depictingalternate implementations of a transmitting operation 608 of FIG. 6,according to one or more embodiments.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar or identical components oritems, unless context dictates otherwise. The illustrative embodimentsdescribed in the detailed description, drawings, and claims are notmeant to be limiting. Other embodiments may be utilized, and otherchanges may be made, without departing from the spirit or scope of thesubject matter presented here.

In accordance with various embodiments, computationally implementedmethods, systems, circuitry, articles of manufacture, and computerprogram products are designed to, among other things, provide aninterface for detecting speech data related to a speech-facilitatedtransaction, acquiring adaptation data that is at least partly based onat least one speech interaction of a particular party that is discretefrom the detected speech data, wherein at least a portion of theadaptation data has been stored on a particular device associated withthe particular party, obtaining a destination of one or more of theadaptation data and the speech data, and transmitting one or more of thespeech data and the adaptation data to the acquired destination.

The present application uses formal outline headings for clarity ofpresentation. However, it is to be understood that the outline headingsare for presentation purposes, and that different types of subjectmatter may be discussed throughout the application (e.g.,device(s)/structure(s) may be described under process(es)/operationsheading(s) and/or process(es)/operations may be discussed understructure(s)/process(es) headings; and/or descriptions of single topicsmay span two or more topic headings). Hence, the use of the formaloutline headings is not intended to be in any way limiting.

Throughout this application, examples and lists are given, withparentheses, the abbreviation “e.g.,” or both. Unless explicitlyotherwise stated, these examples and lists are merely exemplary and arenon-exhaustive. In most cases, it would be prohibitive to list everyexample and every combination. Thus, smaller, illustrative lists andexamples are used, with focus on imparting understanding of the claimterms rather than limiting the scope of such terms.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations are not expressly set forth herein for sakeof clarity.

One skilled in the art will recognize that the herein describedcomponents (e.g., operations), devices, objects, and the discussionaccompanying them are used as examples for the sake of conceptualclarity and that various configuration modifications are contemplated.Consequently, as used herein, the specific exemplars set forth and theaccompanying discussion are intended to be representative of their moregeneral classes. In general, use of any specific exemplar is intended tobe representative of its class, and the non-inclusion of specificcomponents (e.g., operations), devices, and objects should not be takenlimiting.

Although user 105 is shown/described herein as a single illustratedfigure, those skilled in the art will appreciate that user 105 may berepresentative of a human user, a robotic user (e.g., computationalentity), and/or substantially any combination thereof (e.g., a user maybe assisted by one or more robotic agents) unless context dictatesotherwise. Those skilled in the art will appreciate that, in general,the same may be said of “sender” and/or other entity-oriented terms assuch terms are used herein unless context dictates otherwise.

Those having skill in the art will recognize that the state of the arthas progressed to the point where there is little distinction leftbetween hardware, software, and/or firmware implementations of aspectsof systems; the use of hardware, software, and/or firmware is generally(but not always, in that in certain contexts the choice between hardwareand software can become significant) a design choice representing costvs. efficiency tradeoffs. Those having skill in the art will appreciatethat there are various vehicles by which processes and/or systems and/orother technologies described herein can be effected (e.g., hardware,software, and/or firmware), and that the preferred vehicle will varywith the context in which the processes and/or systems and/or othertechnologies are deployed. For example, if an implementer determinesthat speed and accuracy are paramount, the implementer may opt for amainly hardware and/or firmware vehicle; alternatively, if flexibilityis paramount, the implementer may opt for a mainly softwareimplementation; or, yet again alternatively, the implementer may opt forsome combination of hardware, software, and/or firmware in one or moremachines, compositions of matter, and articles of manufacture, limitedto patentable subject matter under 35 USC 101. Hence, there are severalpossible vehicles by which the processes and/or devices and/or othertechnologies described herein may be effected, none of which isinherently superior to the other in that any vehicle to be utilized is achoice dependent upon the context in which the vehicle will be deployedand the specific concerns (e.g., speed, flexibility, or predictability)of the implementer, any of which may vary. Those skilled in the art willrecognize that optical aspects of implementations will typically employoptically-oriented hardware, software, and or firmware.

In some implementations described herein, logic and similarimplementations may include software or other control structures.Electronic circuitry, for example, may have one or more paths ofelectrical current constructed and arranged to implement variousfunctions as described herein. In some implementations, one or moremedia may be configured to bear a device-detectable implementation whensuch media hold or transmit device detectable instructions operable toperform as described herein. In some variants, for example,implementations may include an update or modification of existingsoftware or firmware, or of gate arrays or programmable hardware, suchas by performing a reception of or a transmission of one or moreinstructions in relation to one or more operations described herein.Alternatively or additionally, in some variants, an implementation mayinclude special-purpose hardware, software, firmware components, and/orgeneral-purpose components executing or otherwise invokingspecial-purpose components. Specifications or other implementations maybe transmitted by one or more instances of tangible transmission mediaas described herein, optionally by packet transmission or otherwise bypassing through distributed media at various times.

Alternatively or additionally, implementations may include executing aspecial-purpose instruction sequence or invoking circuitry for enabling,triggering, coordinating, requesting, or otherwise causing one or moreoccurrences of virtually any functional operations described herein. Insome variants, operational or other logical descriptions herein may beexpressed as source code and compiled or otherwise invoked as anexecutable instruction sequence. In some contexts, for example,implementations may be provided, in whole or in part, by source code,such as C++, or other code sequences. In other implementations, sourceor other code implementation, using commercially available and/ortechniques in the art, may be compiled//implemented/translated/convertedinto a high-level descriptor language (e.g., initially implementingdescribed technologies in C or C++ programming language and thereafterconverting the programming language implementation into alogic-synthesizable language implementation, a hardware descriptionlanguage implementation, a hardware design simulation implementation,and/or other such similar mode(s) of expression). For example, some orall of a logical expression (e.g., computer programming languageimplementation) may be manifested as a Verilog-type hardware description(e.g., via Hardware Description Language (HDL) and/or Very High SpeedIntegrated Circuit Hardware Descriptor Language (VHDL)) or othercircuitry model which may then be used to create a physicalimplementation having hardware (e.g., an Application Specific IntegratedCircuit). Those skilled in the art will recognize how to obtain,configure, and optimize suitable transmission or computational elements,material supplies, actuators, or other structures in light of theseteachings.

The claims, description, and drawings of this application may describeone or more of the instant technologies in operational/functionallanguage, for example as a set of operations to be performed by acomputer. Such operational/functional description in most instanceswould be understood by one skilled the art as specifically-configuredhardware (e.g., because a general purpose computer in effect becomes aspecial purpose computer once it is programmed to perform particularfunctions pursuant to instructions from program software).

Importantly, although the operational/functional descriptions describedherein are understandable by the human mind, they are not abstract ideasof the operations/functions divorced from computational implementationof those operations/functions. Rather, the operations/functionsrepresent a specification for the massively complex computationalmachines or other means. As discussed in detail below, theoperational/functional language must be read in its proper technologicalcontext, i.e., as concrete specifications for physical implementations.

The logical operations/functions described herein are a distillation ofmachine specifications or other physical mechanisms specified by theoperations/functions such that the otherwise inscrutable machinespecifications may be comprehensible to the human mind. The distillationalso allows one of skill in the art to adapt the operational/functionaldescription of the technology across many different specific vendors'hardware configurations or platforms, without being limited to specificvendors' hardware configurations or platforms.

Some of the present technical description (e.g., detailed description,drawings, claims, etc.) may be set forth in terms of logicaloperations/functions. As described in more detail in the followingparagraphs, these logical operations/functions are not representationsof abstract ideas, but rather representative of static or sequencedspecifications of various hardware elements. Differently stated, unlesscontext dictates otherwise, the logical operations/functions will beunderstood by those of skill in the art to be representative of staticor sequenced specifications of various hardware elements. This is truebecause tools available to one of skill in the art to implementtechnical disclosures set forth in operational/functional formats—toolsin the form of a high-level programming language (e.g., C, java, visualbasic), etc.), or tools in the form of Very high speed HardwareDescription Language (“VHDL,” which is a language that uses text todescribe logic circuits)—are generators of static or sequencedspecifications of various hardware configurations. This fact issometimes obscured by the broad term “software,” but, as shown by thefollowing explanation, those skilled in the art understand that what istermed “software” is shorthand for a massively complexinterchaining/specification of ordered-matter elements. The term“ordered-matter elements” may refer to physical components ofcomputation, such as assemblies of electronic logic gates, molecularcomputing logic constituents, quantum computing mechanisms, etc.

For example, a high-level programming language is a programming languagewith strong abstraction, e.g., multiple levels of abstraction, from thedetails of the sequential organizations, states, inputs, outputs, etc.,of the machines that a high-level programming language actuallyspecifies. See, e.g., Wikipedia, High-level programming language,http://en.wikipedia.org/wiki/High-level_programming_language (as of Jun.5, 2012, 21:00 GMT). In order to facilitate human comprehension, in manyinstances, high-level programming languages resemble or even sharesymbols with natural languages. See, e.g., Wikipedia, Natural language,http://en.wikipedia.org/wiki/Natural_language (as of Jun. 5, 2012, 21:00GMT).

It has been argued that because high-level programming languages usestrong abstraction (e.g., that they may resemble or share symbols withnatural languages), they are therefore a “purely mental construct.”(e.g., that “software”—a computer program or computer programming—issomehow an ineffable mental construct, because at a high level ofabstraction, it can be conceived and understood in the human mind). Thisargument has been used to characterize technical description in the formof functions/operations as somehow “abstract ideas.” In fact, intechnological arts (e.g., the information and communicationtechnologies) this is not true.

The fact that high-level programming languages use strong abstraction tofacilitate human understanding should not be taken as an indication thatwhat is expressed is an abstract idea. In fact, those skilled in the artunderstand that just the opposite is true. If a high-level programminglanguage is the tool used to implement a technical disclosure in theform of functions/operations, those skilled in the art will recognizethat, far from being abstract, imprecise, “fuzzy,” or “mental” in anysignificant semantic sense, such a tool is instead a nearincomprehensibly precise sequential specification of specificcomputational machines—the parts of which are built up byactivating/selecting such parts from typically more generalcomputational machines over time (e.g., clocked time). This fact issometimes obscured by the superficial similarities between high-levelprogramming languages and natural languages. These superficialsimilarities also may cause a glossing over of the fact that high-levelprogramming language implementations ultimately perform valuable work bycreating/controlling many different computational machines.

The many different computational machines that a high-level programminglanguage specifies are almost unimaginably complex. At base, thehardware used in the computational machines typically consists of sometype of ordered matter (e.g., traditional electronic devices (e.g.,transistors), deoxyribonucleic acid (DNA), quantum devices, mechanicalswitches, optics, fluidics, pneumatics, optical devices (e.g., opticalinterference devices), molecules, etc.) that are arranged to form logicgates. Logic gates are typically physical devices that may beelectrically, mechanically, chemically, or otherwise driven to changephysical state in order to create a physical reality of Boolean logic.

Logic gates may be arranged to form logic circuits, which are typicallyphysical devices that may be electrically, mechanically, chemically, orotherwise driven to create a physical reality of certain logicalfunctions. Types of logic circuits include such devices as multiplexers,registers, arithmetic logic units (ALUs), computer memory, etc., eachtype of which may be combined to form yet other types of physicaldevices, such as a central processing unit (CPU)—the best known of whichis the microprocessor. A modern microprocessor will often contain morethan one hundred million logic gates in its many logic circuits (andoften more than a billion transistors). See, e.g., Wikipedia, Logicgates, http://en.wikipedia.org/wiki/Logic_gates (as of Jun. 5, 2012,21:03 GMT).

The logic circuits forming the microprocessor are arranged to provide amicroarchitecture that will carry out the instructions defined by thatmicroprocessor's defined Instruction Set Architecture. The InstructionSet Architecture is the part of the microprocessor architecture relatedto programming, including the native data types, instructions,registers, addressing modes, memory architecture, interrupt andexception handling, and external Input/Output. See, e.g., Wikipedia,Computer architecture,http://en.wikipedia.org/wiki/Computer_architecture (as of Jun. 5, 2012,21:03 GMT).

The Instruction Set Architecture includes a specification of the machinelanguage that can be used by programmers to use/control themicroprocessor. Since the machine language instructions are such thatthey may be executed directly by the microprocessor, typically theyconsist of strings of binary digits, or bits. For example, a typicalmachine language instruction might be many bits long (e.g., 32, 64, or128 bit strings are currently common). A typical machine languageinstruction might take the form “11110000101011110000111100111111” (a 32bit instruction).

It is significant here that, although the machine language instructionsare written as sequences of binary digits, in actuality those binarydigits specify physical reality. For example, if certain semiconductorsare used to make the operations of Boolean logic a physical reality, theapparently mathematical bits “1” and “0” in a machine languageinstruction actually constitute shorthand that specifies the applicationof specific voltages to specific wires. For example, in somesemiconductor technologies, the binary number “1” (e.g., logical “1”) ina machine language instruction specifies around +5 volts applied to aspecific “wire” (e.g., metallic traces on a printed circuit board) andthe binary number “0” (e.g., logical “0”) in a machine languageinstruction specifies around −5 volts applied to a specific “wire.” Inaddition to specifying voltages of the machines' configuration, suchmachine language instructions also select out and activate specificgroupings of logic gates from the millions of logic gates of the moregeneral machine. Thus, far from abstract mathematical expressions,machine language instruction programs, even though written as a stringof zeros and ones, specify many, many constructed physical machines orphysical machine states.

Machine language is typically incomprehensible by most humans (e.g., theabove example was just ONE instruction, and some personal computersexecute more than two billion instructions every second). See, e.g.,Wikipedia, Instructions per second,http://en.wikipedia.org/wiki/Instructions_per_second (as of Jun. 5,2012, 21:04 GMT). Thus, programs written in machine language—which maybe tens of millions of machine language instructions long—areincomprehensible. In view of this, early assembly languages weredeveloped that used mnemonic codes to refer to machine languageinstructions, rather than using the machine language instructions'numeric values directly (e.g., for performing a multiplicationoperation, programmers coded the abbreviation “mult,” which representsthe binary number “011000” in MIPS machine code). While assemblylanguages were initially a great aid to humans controlling themicroprocessors to perform work, in time the complexity of the work thatneeded to be done by the humans outstripped the ability of humans tocontrol the microprocessors using merely assembly languages.

At this point, it was noted that the same tasks needed to be done overand over, and the machine language necessary to do those repetitivetasks was the same. In view of this, compilers were created. A compileris a device that takes a statement that is more comprehensible to ahuman than either machine or assembly language, such as “add 2+2 andoutput the result,” and translates that human understandable statementinto a complicated, tedious, and immense machine language code (e.g.,millions of 32, 64, or 128 bit length strings). Compilers thus translatehigh-level programming language into machine language.

This compiled machine language, as described above, is then used as thetechnical specification which sequentially constructs and causes theinteroperation of many different computational machines such thathumanly useful, tangible, and concrete work is done. For example, asindicated above, such machine language—the compiled version of thehigher-level language—functions as a technical specification whichselects out hardware logic gates, specifies voltage levels, voltagetransition timings, etc., such that the humanly useful work isaccomplished by the hardware.

Thus, a functional/operational technical description, when viewed by oneof skill in the art, is far from an abstract idea. Rather, such afunctional/operational technical description, when understood throughthe tools available in the art such as those just described, is insteadunderstood to be a humanly understandable representation of a hardwarespecification, the complexity and specificity of which far exceeds thecomprehension of most any one human. With this in mind, those skilled inthe art will understand that any such operational/functional technicaldescriptions—in view of the disclosures herein and the knowledge ofthose skilled in the art—may be understood as operations made intophysical reality by (a) one or more interchained physical machines, (b)interchained logic gates configured to create one or more physicalmachine(s) representative of sequential/combinatorial logic(s), (c)interchained ordered matter making up logic gates (e.g., interchainedelectronic devices (e.g., transistors), DNA, quantum devices, mechanicalswitches, optics, fluidics, pneumatics, molecules, etc.) that createphysical reality representative of logic(s), or (d) virtually anycombination of the foregoing. Indeed, any physical object which has astable, measurable, and changeable state may be used to construct amachine based on the above technical description. Charles Babbage, forexample, constructed the first computer out of wood and powered bycranking a handle.

Thus, far from being understood as an abstract idea, those skilled inthe art will recognize a functional/operational technical description asa humanly-understandable representation of one or more almostunimaginably complex and time sequenced hardware instantiations. Thefact that functional/operational technical descriptions might lendthemselves readily to high-level computing languages (or high-levelblock diagrams for that matter) that share some words, structures,phrases, etc. with natural language simply cannot be taken as anindication that such functional/operational technical descriptions areabstract ideas, or mere expressions of abstract ideas. In fact, asoutlined herein, in the technological arts this is simply not true. Whenviewed through the tools available to those of skill in the art, suchfunctional/operational technical descriptions are seen as specifyinghardware configurations of almost unimaginable complexity.

As outlined above, the reason for the use of functional/operationaltechnical descriptions is at least twofold. First, the use offunctional/operational technical descriptions allows near-infinitelycomplex machines and machine operations arising from interchainedhardware elements to be described in a manner that the human mind canprocess (e.g., by mimicking natural language and logical narrativeflow). Second, the use of functional/operational technical descriptionsassists the person of skill in the art in understanding the describedsubject matter by providing a description that is more or lessindependent of any specific vendor's piece(s) of hardware.

The use of functional/operational technical descriptions assists theperson of skill in the art in understanding the described subject mattersince, as is evident from the above discussion, one could easily,although not quickly, transcribe the technical descriptions set forth inthis document as trillions of ones and zeroes, billions of single linesof assembly-level machine code, millions of logic gates, thousands ofgate arrays, or any number of intermediate levels of abstractions.However, if any such low-level technical descriptions were to replacethe present technical description, a person of skill in the art couldencounter undue difficulty in implementing the disclosure, because sucha low-level technical description would likely add complexity without acorresponding benefit (e.g., by describing the subject matter utilizingthe conventions of one or more vendor-specific pieces of hardware).Thus, the use of functional/operational technical descriptions assiststhose of skill in the art by separating the technical descriptions fromthe conventions of any vendor-specific piece of hardware.

In view of the foregoing, the logical operations/functions set forth inthe present technical description are representative of static orsequenced specifications of various ordered-matter elements, in orderthat such specifications may be comprehensible to the human mind andadaptable to create many various hardware configurations. The logicaloperations/functions disclosed herein should be treated as such, andshould not be disparagingly characterized as abstract ideas merelybecause the specifications they represent are presented in a manner thatone of skill in the art can readily understand and apply in a mannerindependent of a specific vendor's hardware implementation.

Those skilled in the art will recognize that it is common within the artto implement devices and/or processes and/or systems, and thereafter useengineering and/or other practices to integrate such implemented devicesand/or processes and/or systems into more comprehensive devices and/orprocesses and/or systems. That is, at least a portion of the devicesand/or processes and/or systems described herein can be integrated intoother devices and/or processes and/or systems via a reasonable amount ofexperimentation. Those having skill in the art will recognize thatexamples of such other devices and/or processes and/or systems mightinclude—as appropriate to context and application—all or part of devicesand/or processes and/or systems of (a) an air conveyance (e.g., anairplane, rocket, helicopter, etc.), (b) a ground conveyance (e.g., acar, truck, locomotive, tank, armored personnel carrier, etc.), (c) abuilding (e.g., a home, warehouse, office, etc.), (d) an appliance(e.g., a refrigerator, a washing machine, a dryer, etc.), (e) acommunications system (e.g., a networked system, a telephone system, aVoice over IP system, etc.), (f) a business entity (e.g., an InternetService Provider (ISP) entity such as Comcast Cable, Qwest, SouthwesternBell, etc.), or (g) a wired/wireless services entity (e.g., Sprint,Cingular, Nextel, etc.), etc.

In certain cases, use of a system or method may occur in a territoryeven if components are located outside the territory. For example, in adistributed computing context, use of a distributed computing system mayoccur in a territory even though parts of the system may be locatedoutside of the territory (e.g., relay, server, processor, signal-bearingmedium, transmitting computer, receiving computer, etc. located outsidethe territory).

A sale of a system or method may likewise occur in a territory even ifcomponents of the system or method are located and/or used outside theterritory. Further, implementation of at least part of a system forperforming a method in one territory does not preclude use of the systemin another territory.

One skilled in the art will recognize that the herein describedcomponents (e.g., operations), devices, objects, and the discussionaccompanying them are used as examples for the sake of conceptualclarity and that various configuration modifications are contemplated.Consequently, as used herein, the specific exemplars set forth and theaccompanying discussion are intended to be representative of their moregeneral classes. In general, use of any specific exemplar is intended tobe representative of its class, and the non-inclusion of specificcomponents (e.g., operations), devices, and objects should not be takenlimiting.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely exemplary, and that in fact many other architectures may beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected”, or“operably coupled,” to each other to achieve the desired functionality,and any two components capable of being so associated can also be viewedas being “operably couplable,” to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents, and/or wirelessly interactable, and/or wirelesslyinteracting components, and/or logically interacting, and/or logicallyinteractable components.

In some instances, one or more components may be referred to herein as“configured to,” “configured by,” “configurable to,” “operable/operativeto,” “adapted/adaptable,” “able to,” “conformable/conformed to,” etc.Those skilled in the art will recognize that such terms (e.g.“configured to”) generally encompass active-state components and/orinactive-state components and/or standby-state components, unlesscontext requires otherwise.

In a general sense, those skilled in the art will recognize that thevarious embodiments described herein can be implemented, individuallyand/or collectively, by various types of electro-mechanical systemshaving a wide range of electrical components such as hardware, software,firmware, and/or virtually any combination thereof, limited topatentable subject matter under 35 U.S.C. 101; and a wide range ofcomponents that may impart mechanical force or motion such as rigidbodies, spring or torsional bodies, hydraulics, electro-magneticallyactuated devices, and/or virtually any combination thereof.Consequently, as used herein “electro-mechanical system” includes, butis not limited to, electrical circuitry operably coupled with atransducer (e.g., an actuator, a motor, a piezoelectric crystal, a MicroElectro Mechanical System (MEMS), etc.), electrical circuitry having atleast one discrete electrical circuit, electrical circuitry having atleast one integrated circuit, electrical circuitry having at least oneapplication specific integrated circuit, electrical circuitry forming ageneral purpose computing device configured by a computer program (e.g.,a general purpose computer configured by a computer program which atleast partially carries out processes and/or devices described herein,or a microprocessor configured by a computer program which at leastpartially carries out processes and/or devices described herein),electrical circuitry forming a memory device (e.g., forms of memory(e.g., random access, flash, read only, etc.)), electrical circuitryforming a communications device (e.g., a modem, communications switch,optical-electrical equipment, etc.), and/or any non-electrical analogthereto, such as optical or other analogs (e.g., graphene basedcircuitry). Those skilled in the art will also appreciate that examplesof electro-mechanical systems include but are not limited to a varietyof consumer electronics systems, medical devices, as well as othersystems such as motorized transport systems, factory automation systems,security systems, and/or communication/computing systems. Those skilledin the art will recognize that electro-mechanical as used herein is notnecessarily limited to a system that has both electrical and mechanicalactuation except as context may dictate otherwise.

In a general sense, those skilled in the art will recognize that thevarious aspects described herein which can be implemented, individuallyand/or collectively, by a wide range of hardware, software, firmware,and/or any combination thereof can be viewed as being composed ofvarious types of “electrical circuitry.” Consequently, as used herein“electrical circuitry” includes, but is not limited to, electricalcircuitry having at least one discrete electrical circuit, electricalcircuitry having at least one integrated circuit, electrical circuitryhaving at least one application specific integrated circuit, electricalcircuitry forming a general purpose computing device configured by acomputer program (e.g., a general purpose computer configured by acomputer program which at least partially carries out processes and/ordevices described herein, or a microprocessor configured by a computerprogram which at least partially carries out processes and/or devicesdescribed herein), electrical circuitry forming a memory device (e.g.,forms of memory (e.g., random access, flash, read only, etc.)), and/orelectrical circuitry forming a communications device (e.g., a modem,communications switch, optical-electrical equipment, etc.). Those havingskill in the art will recognize that the subject matter described hereinmay be implemented in an analog or digital fashion or some combinationthereof.

Those skilled in the art will recognize that at least a portion of thedevices and/or processes described herein can be integrated into a dataprocessing system. Those having skill in the art will recognize that adata processing system generally includes one or more of a system unithousing, a video display device, memory such as volatile or non-volatilememory, processors such as microprocessors or digital signal processors,computational entities such as operating systems, drivers, graphicaluser interfaces, and applications programs, one or more interactiondevices (e.g., a touch pad, a touch screen, an antenna, etc.), and/orcontrol systems including feedback loops and control motors (e.g.,feedback for sensing position and/or velocity; control motors for movingand/or adjusting components and/or quantities). A data processing systemmay be implemented utilizing suitable commercially available components,such as those typically found in data computing/communication and/ornetwork computing/communication systems.

For the purposes of this application, “cloud” computing may beunderstood as described in the cloud computing literature. For example,cloud computing may be methods and/or systems for the delivery ofcomputational capacity and/or storage capacity as a service. The “cloud”may refer to one or more hardware and/or software components thatdeliver or assist in the delivery of computational and/or storagecapacity, including, but not limited to, one or more of a client, anapplication, a platform, an infrastructure, and/or a server The cloudmay refer to any of the hardware and/or software associated with aclient, an application, a platform, an infrastructure, and/or a server.For example, cloud and cloud computing may refer to one or more of acomputer, a processor, a storage medium, a router, a switch, a modem, avirtual machine (e.g., a virtual server), a data center, an operatingsystem, a middleware, a firmware, a hardware back-end, a softwareback-end, and/or a software application. A cloud may refer to a privatecloud, a public cloud, a hybrid cloud, and/or a community cloud. A cloudmay be a shared pool of configurable computing resources, which may bepublic, private, semi-private, distributable, scalable, flexible,temporary, virtual, and/or physical. A cloud or cloud service may bedelivered over one or more types of network, e.g., a mobilecommunication network, and the Internet.

As used in this application, a cloud or a cloud service may include oneor more of infrastructure-as-a-service (“IaaS”), platform-as-a-service(“PaaS”), software-as-a-service (“SaaS”), and/or desktop-as-a-service(“DaaS”). As a non-exclusive example, IaaS may include, e.g., one ormore virtual server instantiations that may start, stop, access, and/orconfigure virtual servers and/or storage centers (e.g., providing one ormore processors, storage space, and/or network resources on-demand,e.g., EMC and Rackspace). PaaS may include, e.g., one or more softwareand/or development tools hosted on an infrastructure (e.g., a computingplatform and/or a solution stack from which the client can createsoftware interfaces and applications, e.g., Microsoft Azure). SaaS mayinclude, e.g., software hosted by a service provider and accessible overa network (e.g., the software for the application and/or the dataassociated with that software application may be kept on the network,e.g., Google Apps, SalesForce). DaaS may include, e.g., providingdesktop, applications, data, and/or services for the user over a network(e.g., providing a multi-application framework, the applications in theframework, the data associated with the applications, and/or servicesrelated to the applications and/or the data over the network, e.g.,Citrix). The foregoing is intended to be exemplary of the types ofsystems and/or methods referred to in this application as “cloud” or“cloud computing” and should not be considered complete or exhaustive.

The proliferation of automation in many transactions is apparent. Forexample, Automated Teller Machines (“ATMs”) dispense money and receivedeposits. Airline ticket counter machines check passengers in, dispensetickets, and allow passengers to change or upgrade flights. Train andsubway ticket counter machines allow passengers to purchase a ticket toa particular destination without invoking a human interaction at all.Many groceries and pharmacies have self-service checkout machines whichallow a consumer to pay for goods purchased by interacting only with amachine. Large companies now staff telephone answering systems withmachines that interact with customers, and invoke a human in thetransaction only if there is a problem with the machine-facilitatedtransaction.

Nevertheless, as such automation increases, convenience andaccessibility may decrease. Self-checkout machines at grocery stores maybe difficult to operate. ATMs and ticket counter machines may be mostlyinaccessible to disabled persons or persons requiring special access.Where before, the interaction with a human would allow disabled personsto complete transactions with relative ease, if a disabled person isunable to push the buttons on an ATM, there is little the machine can doto facilitate the transaction to completion. While some of these publicterminals allow speech operations, they are configured to the mostgeneric forms of speech, which may be less useful in recognizingparticular speakers, thereby leading to frustration for users attemptingto speak to the machine. This problem may be especially challenging forthe disabled, who already may face significant challenges in completingtransactions with automated machines.

In addition, smartphones and tablet devices also now are configured toreceive speech commands. Speech and voice controlled automobile systemsnow appear regularly in motor vehicles, even in economical,mass-produced vehicles. Home entertainment devices, e.g., disc players,televisions, radios, stereos, and the like, may respond to speechcommands. Additionally, home security systems may respond to speechcommands. In an office setting, a worker's computer may respond tospeech from that worker, allowing faster, more efficient work flows.Such systems and machines may be trained to operate with particularusers, either through explicit training or through repeatedinteractions. Nevertheless, when that system is upgraded or replaced,e.g., a new television is purchased, that training may be lost with thedevice. Thus, in some embodiments described herein, adaptation data forspeech recognition systems may be separated from the device whichrecognizes the speech, and may be more closely associated with a user,e.g., through a device carried by the user, or through a networklocation associated with the user.

Further, in some environments, there may be more than one device thattransmits and receives data within a range of interacting with a user.For example, merely sitting on a couch watching television may involvefive or more devices, e.g., a television, a cable box, an audio/visualreceiver, a remote control, and a smartphone device. Some of thesedevices may transmit or receive speech data. Some of these devices maytransmit, receive, or store adaptation data, as will be described inmore detail herein. Thus, in some embodiments, which will be describedin more detail herein, there may be methods, systems, and devices fordetermining which devices in a system should perform actions that allowa user to efficiently interact with an intended device through thatuser's speech.

Referring now to FIG. 1, e.g., FIG. 1A, FIG. 1A illustrates an exampleenvironment 100 in which the methods, systems, circuitry, articles ofmanufacture, and computer program products and architecture, inaccordance with various embodiments, may be implemented by one or moreof personal device 20A, personal device 20B, intermediate device 40,target device 30A, and target device 30B. In some embodiments, e.g., asshown in FIG. 1B, personal device 120, which in some embodiments, may bean example of one of personal device 20A, personal device 20B, andintermediate device 40. The personal device 120, in various embodiments,may be endowed with logic that is designed for detecting speech datarelated to a speech-facilitated transaction, logic that is designed foracquiring adaptation data that is at least partly based on at least onespeech interaction of a particular party that is discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on a particular device associated with the particularparty, logic that is designed for obtaining a destination of one or moreof the adaptation data and the speech data, and transmitting one or moreof the speech data and the adaptation data to the acquired destination.

Referring again to the exemplary embodiment in FIG. 1A, a user 105 mayengage in a speech facilitated transaction with one or more of aterminal device 30A and a terminal device 30B. In some embodiments, thespeech-facilitated transaction may be directed to one of terminal device30A or terminal device 30B. In some embodiments, the user may notspecifically direct her speech toward terminal device 30A or terminaldevice 30B, but rather to both of them, with indifference toward whichdevice carries out the speech-facilitated transaction. In someembodiments, one of the terminal device 30A and terminal device 30Bnegotiate between themselves to determine which device will carry outthe speech-facilitated transaction. In some embodiments, one or more ofthe personal device 20A, the personal device 20B, and the intermediatedevice 40 may determine which of the terminal device 30A and terminaldevice 30B carries out the speech-facilitated transaction. In someembodiments, one or more of personal device 20A, personal device 20B,and intermediate device 40 may detect one or more of terminal device 30Aand terminal device 30B, establish a connection, or negotiate with oneor more of terminal devices 30A and 30B.

The dashed-line arrows shown in environment 100 of FIG. 1A are notlabeled, but are intended to show the flow of data from one device tothe other. Some data connections are omitted for simplicity of drawing,e.g., although there is no arrow, personal device 20A may communicatedirectly with terminal device 30A and terminal device 30B. The flow ofdata may include one or more adaptation data, speech data in any format,including raw speech from the user, adaptation result data, intendedtarget data, target data, and the like. The dotted line arrows show anassociation between the user 105 and one or more of personal device 20A,personal device 20B, and intermediate device 40.

Although it is not shown in FIG. 1A, any or all of personal devices 20A,20B, and 40 may communicate with any or all of terminal device 30A andterminal device 30B, either directly, or indirectly. In someembodiments, these devices communicate with each other via a server 110,which may be local or remote to any of the devices 20A, 20B, 30A, 30B,and 40. In some embodiments, these devices communicate with each othervia one or more communication networks 140, which may be local or remoteto any of the devices 20A, 20B, 30A, 30B, and 40. Although server 110and communication network 40 are pictured in each of the embodiments inFIGS. 1A and 1C-1E, server 110 and communication network 140 are notrequired, and are shown merely for purposes of illustration.

Referring again to FIG. 1A, FIG. 1A shows personal device 20A, personaldevice 20B, intermediate device 40, terminal device 30A, terminal device30B, and server 110. The number of devices is shown merely forillustrative purposes. In some embodiments, however, there may be adifferent number of personal devices, intermediate devices, terminaldevices, servers, and communication networks. In some embodiments, oneor more of the personal devices, intermediate devices, terminal devices,servers, and communication networks may be omitted entirely.

Referring again to FIG. 1A, personal device 20A and 20B are shown asassociated with user 105. This association may be attenuated, e.g., theymay merely be in the same physical proximity. In other embodiments, theassociation may be one of ownership, mutual contract, informationstoring, previous usage, or other factors. The examples describedfurther herein will provide a non-exhaustive list of examples ofrelationships between user 105 and a personal device 20. In someembodiments, personal device 20 may be any size and have anyspecification. Personal device 20 may be a custom device of any shape orsize, configured to transmit, receive, and store data. Personal device20 may include, but is not limited to, a smartphone device, a tabletdevice, a personal computer device, a laptop device, a keychain device,a key, a personal digital assistant device, a modified memory stick, auniversal remote control, or any other piece of electronics. Inaddition, personal device 20 may be a modified object that is worn,e.g., eyeglasses, a wallet, a credit card, a watch, a chain, or anarticle of clothing. Anything that is configured to store, transmit, andreceive data may be a personal device 20, and personal device 20 is notlimited in size to devices that are capable of being carried by a user.Additionally, personal device 20 may not be in direct proximity to theuser, e.g., personal device 20 may be a computer sitting on a desk in auser's home or office.

Although terminal devices 30A and 30B are described as “terminaldevice,” this is merely for simplicity of illustration. terminal device30 could be any device that is configured to receive speech. Forexample, terminal device 30 may be a terminal, a computer, a navigationsystem, a phone, a piece of home electronics (e.g., a DVD player,Blu-Ray player, media player, game system, television, receiver, alarmclock, and the like). Terminal device 30 may, in some embodiments, be ahome security system, a safe lock, a door lock, a kitchen applianceconfigured to receive speech, and the like. In some embodiments,terminal device 30 may be a motorized vehicle, e.g., a car, boat,airplane, motorcycle, golf cart, wheelchair, and the like. In someembodiments, terminal device 30 may be a piece of portable electronics,e.g., a laptop computer, a netbook computer, a tablet device, asmartphone, a cellular phone, a radio, a portable navigation system, orany other piece of electronics capable of receiving speech. Terminaldevice 30 may be a part of an enterprise solution, e.g., a commonworkstation in an office, a copier, a scanner, a personal workstation ina cubicle, an office directory, an interactive screen, and a telephone.These examples and lists are not meant to be exhaustive, but merely toillustrate a few examples of the terminal device. Some of these examplesare shown in more detail with respect to FIGS. 1C, 1D, and 1E.

In some embodiments, terminal 30 receives adaptation data from thepersonal device 20, in a process that will be described in more detailherein. In some embodiments, the adaptation data is transmitted over oneor more communication network(s) 40. In various embodiments, thecommunication network 40 may include one or more of a local area network(LAN), a wide area network (WAN), a metropolitan area network (MAN), awireless local area network (WLAN), a personal area network (PAN), aWorldwide Interoperability for Microwave Access (WiMAX), public switchedtelephone network (PTSN), a general packet radio service (GPRS) network,a cellular network, and so forth. The communication networks 40 may bewired, wireless, or a combination of wired and wireless networks. It isnoted that “communication network” here refers to one or morecommunication networks, which may or may not interact with each other.

In some embodiments, the adaptation data does not come directly from thepersonal device 20. In some embodiments, personal device 20 merelyfacilitates communication of the adaptation data, e.g., by providing oneor more of an address, credentials, instructions, authorization, andrecommendations. For example, in some embodiments, personal device 20provides a location at server 10 at which adaptation data may bereceived. In some embodiments, personal device 20 retrieves adaptationdata from server 10 upon a request from the terminal device 30, and thenrelays or facilitates in the relaying of the adaptation data to terminaldevice 30.

In some embodiments, personal device 20 broadcasts the adaptation dataregardless of whether a terminal device 30 is listening, e.g., atpredetermined, regular, or otherwise-defined intervals. In otherembodiments, personal device 20 listens for a request from a terminaldevice 30, and transmits or broadcasts adaptation data in response tothat request. In some embodiments, user 5 determines when personaldevice 20 broadcasts adaptation data. In still other embodiments, athird party (not shown) triggers the transmission of adaptation data tothe terminal device 30, in which the transmission is facilitated by thepersonal device 20.

FIG. 1B shows a more detailed description of a personal device 120 in anexemplary environment 100. Personal device 120 may be an example ofpersonal device 20A or 20B of FIG. 1A, intermediate device 40 of FIG.1A, first personal device 21A of FIG. 1C, second personal device 21B ofFIG. 1D, one of the modules of device 31 of FIG. 1C, personal device 22Aof FIG. 1D, personal device 22B of FIG. 1D, any of devices 51, 52, 53,and 54 of FIG. 1D, smart key device 26 of FIG. 1E, GPS navigation device41 of FIG. 1E, and the like. The foregoing is not intended to beexhaustive of the possible devices that correspond to personal device120 of FIG. 1B, but are merely exemplary of the types of devices thatmay have a structure as outlined in FIG. 1B.

Referring again to FIG. 1B, in various embodiments, the personal device120 may comprise, among other elements, a processor 132, a memory 134, auser interface 135, a speech detection interface 138, and a datatransmission interface 137. Each of these elements may be absent invarious embodiments of personal device 120, e.g., some personal devices120 may not have a speech detection interface 138, or a memory 134, or auser interface 135.

Processor 132 may include one or more microprocessors, CentralProcessing Units (“CPU”), a Graphics Processing Units (“GPU”), PhysicsProcessing Units, Digital Signal Processors, Network Processors,Floating Point Processors, and the like. In some embodiments, processor132 may be a server. In some embodiments, processor 132 may be adistributed-core processor. Although processor 132 is as a singleprocessor that is part of a single personal device 120, processor 132may be multiple processors distributed over one or many computingdevices 30, which may or may not be configured to operate together.Processor 132 is illustrated as being configured to execute computerreadable instructions in order to execute one or more operationsdescribed above, and as illustrated in FIGS. 6, 7A-7D, 8A-8Q, 9A-9D, and10A-10C. In some embodiments, processor 132 is designed to be configuredto operate as processing module 150, which may include one or more ofspeech data related to speech facilitated transaction detecting module152, adaptation data at least partly based on discrete speechinteraction of particular party separate from detected speech data, andhas been stored on a particular party-associated particular deviceacquiring module 154, destination of one or more of the adaptation dataand the speech data acquiring module 156, and acquired destination ofone or more of the adaptation data and the speech data transmittingmodule 158.

Referring again to FIG. 1B, as set forth above, personal device 120 mayinclude a memory 134. In some embodiments, memory 134 may comprise ofone or more of one or more mass storage devices, read-only memory (ROM),programmable read-only memory (PROM), erasable programmable read-onlymemory (EPROM), cache memory such as random access memory (RAM), flashmemory, synchronous random access memory (SRAM), dynamic random accessmemory (DRAM), and/or other types of memory devices. In someembodiments, memory 134 may be located at a single network site. In someembodiments, memory 134 may be located at multiple network sites,including sites that are distant from each other.

Referring again to FIG. 1B, as set forth above, personal device 120 mayinclude a user interface 135. The user interface may be implemented inhardware or software, or both, and may include various input and outputdevices to allow an operator of personal device 120 to interact withpersonal device 120. For example, user interface 135 may include, but isnot limited to, an audio display, e.g., a speaker 108, a video display,e.g., a screen 102, a microphone, a camera, a keyboard, e.g., keyboard103, a trackball, e.g., trackball 104, a mouse, e.g., mouse 105, one ormore soft keys, e.g., hard/soft keys 106, a touch input, e.g.,touchscreen 107, e.g., which may also be a video display screen, ajoystick, a game controller, a touchpad, a handset, or any other devicethat allows interaction between a device and a user.

Referring again to FIG. 1B, as set forth above, personal device 120 mayinclude a speech detection interface 138. Speech detection interface 138may be configured to receive and/or process speech as input, or toobserve and/or record speech of a speech-facilitated transactionAlthough not present in some embodiments, in some embodiments, a speechdetection interface 138 may include a speech indicator receiver 112,which may be a sensor of any type, or a communication port that receivesa signal, or a sensor that detects a button press, or any other modulethat can detect a change of state of any kind in the environment 100,whether internal or external to the device. The speech detectioninterface 138 may, in some embodiments, include a microphone 110, whichmay or may not communicate with speech indicator receiver 112. In someembodiments, microphone 110 may detect speech, either selectively oralways-on, and may be controlled by one or more of speech indicatorreceiver 112 and processor 132.

Referring again to FIG. 1B, as set forth above, personal device 120 mayinclude a data transmission interface 137. Data transmission interface137 may, in some embodiments, handle the transmission and reception ofdata by the device. For example, in some embodiments, data transmissioninterface 137 may include an adaptation data transmitter/receiver 114,which handles the reception and transmission of adaptation data over anytype of network or internal form of communication, e.g., internal bus,and the like. Data transmission interface 137 may, in some embodiments,include speech data transmitter/receiver 116, which may handle thereception and transmission of speech data, including raw speech, overany form of moving data.

Referring again to FIG. 1B, as set forth above, personal device 120 mayhave one or more sensors 182. These sensors include, but are not limitedto, a Global Positioning System (GPS) sensor, a still camera, a videocamera, an altimeter, an air quality sensor, a barometer, anaccelerometer, a charge-coupled device, a radio, a thermometer, apedometer, a heart monitor, a moisture sensor, a humidity sensor, amicrophone, a seismometer, and a magnetic field sensor. Sensors 182 mayinterface with sensor interface 180. Although FIG. 1B illustratessensors 182 as part of personal device 120, in some embodiments, sensors182 may be separated from personal device 120, and communicate via oneor more communication networks, e.g., communication networks 140.

Referring now to FIG. 1C, FIG. 1C shows an example embodiment of anexemplary environment 100′, which is a non-limiting example of anenvironment 100. As shown in FIG. 1C, environment 100′ may include auser (not shown), which user may have one or more of a first personaldevice 21A and a second personal device 21B. First personal device 21Amay be, for example, a USB drive, and second personal device 21B may be,for example, a cellular telephone device, although both personal device21A and personal device 21B may be any form of personal device 120 aspreviously described. One or more of first personal device 21A andsecond personal device 21B may interact with device 31, which may be anytype of computing device, e.g., laptop computer, desktop computer,server, netbook, tablet device, smartphone, and the like. Device 31 mayhave an operating system software 81 loaded thereon. Operating systemsoftware 81 may include, but is not limited to, Microsoft Windows,Google Android, Apple iOS, Apple Mountain Lion, UNIX, Linux, Chrome OS,Symbian, and the like.

In addition, in some embodiments, device 31 may include an enterpriseclient software 82 onboard. For example, some systems, e.g., in anoffice environment, may have a client software, e.g., Citrix, or thelike, loaded on their systems to integrate the user experience for theirworkers. In some embodiments, this module may play a role in determiningthe role of the interpretation of speech data (e.g., speech data 101)and the application of adaptation data. In some embodiments, device 31also may include one or more of first application software 91 and secondapplication software 92. First and second application software 91 and 92may be any type of application, e.g., game, spreadsheet, word processor,web browser, chat client, picture viewer, picture manipulator, webcamapplication, and the like. In some embodiments, these modules may play arole in determining the role of the interpretation of speech data andthe application of adaptation data. For example, the complexity of theapplication may play a role in determining how much of the speechprocessing occurs at the application level. In some embodiments, device31 may communicate with one or more communication networks 140 and oneor more servers 110.

Referring now to FIG. 1D, FIG. 1D shows an example embodiment of anexemplary environment 100″, which is a non-limiting example of anenvironment 100. As shown in FIG. 1D, environment 100″ may include auser 105, which user may have one or more of a personal device 22A and apersonal device 22B. Personal device 22A may be, for example, auniversal remote control, and personal device 22B may be, for example, acellular telephone device, although both personal device 22A andpersonal device 22B may be any form of personal device 120 as previouslydescribed. In some embodiments, one or both of personal device 22A,personal device 22B, and computing device 54 may transmit, store, and/orreceive adaptation data. In some embodiments, one of personal device22A, personal device 22B, and computing device 54 may determine to whichof the devices shown in FIG. 1D the user 105 is directing her speech. Inother embodiments, one or more of receiver device 51, media playerdevice 52, and television device 53 may transmit one or more of speechdata and adaptation data back and forth, and one or more of receiverdevice 51, media player device 52, and television device 53 maydetermine which device should apply the adaptation data, and whichdevice should process the speech data, out of devices 22A, 22B, 51, 52,53, and 54.

Referring now to FIG. 1E, FIG. 1E shows an example embodiment of anexemplary environment 100′″, which is a non-limiting example of anenvironment 100. As shown in FIG. 1E, environment 100′″ may include auser (not shown) driving an automobile (interior only shown), whereinthe automobile is equipped with a motor vehicle control system 42, whichmay control the non-driving features of the automobile, e.g., music,climate, temperature, fuel management, seat position, media playing,lights, and the like. The automobile also may have a smart key device26, which, in some embodiments, may store, receive, and/or transmitadaptation data, either wirelessly or through the system of theautomobile. In some embodiments, environment 100′″ may also include aGPS navigation device 41, which may be an example of intermediate device40, which also may be a personal device 120. In some embodiments, GPSnavigation device 41 may serve as a terminal device, receiving speechdata and adaptation data in order to process a user's request. In otherembodiments, GPS navigation device 41 may serve as a personal device120, storing adaptation data derived from navigation commands of theuser, and transmitting the adaptation data to a target device, e.g.,motor vehicle control system 42, when needed. Intermediate devices 40,e.g., as shown in FIG. 1A, and GPS navigation device 41, which may be anexample of intermediate device 40, may be a personal device 120 for afirst transaction and a terminal device in a second transaction. In someembodiments, GPS navigation device 41 may change its role based on ananalysis of data received by GPS navigation device 41.

Referring again to FIG. 1E, in some embodiments, GPS navigation device41, motor vehicle control system 42, smart key device 26, and the user'spersonal device (not shown) may communicate with one or morecommunication networks 140 and one or more servers 110. As in all shownexemplary embodiments, however, these elements are optional and someembodiments may exclude them.

Referring now to FIG. 2, FIG. 2 illustrates an exemplary implementationof the speech data related to speech facilitated transaction detectingmodule 152. As illustrated in FIG. 2, the speech data related to speechfacilitated transaction detecting module 152 may include one or moresub-logic modules in various alternative implementations andembodiments. For example, as shown in FIG. 2, e.g., FIG. 2A, in someembodiments, module 152 may include one or more of speech-facilitatedtransaction occurrence detecting module 201, speech-facilitatedtransaction about to occur detecting module 202 (e.g., which, in someembodiments, may include device microphone receiving speech of aspeech-facilitated transaction detecting module 204), indicator ofdevice microphone receiving speech of a speech facilitated transactionsignal detecting module 206, adaptation data receiving module 214, andreception of adaptation data-based speech data transferringdetermination module 216 (e.g., which, in some embodiments, may includereception of adaptation data comprising indicator based speech datatransferring determination module 218). In some embodiments, module 206may include one or more of indicator of particular device microphonereceiving speech of a speech facilitated transaction signal detectingmodule 208, indicator of other device microphone receiving speech of aspeech facilitated transaction signal detecting module 210, indicator ofother device microphone receiving speech of a speech facilitatedtransaction signal detecting module 212.

Referring again to FIG. 2, e.g., FIG. 2B, in some embodiments, module152 may include one or more of signal requesting initiation of one ormore speech-facilitated transaction operations receiving module 220 andspeech data transmission by device detecting module 232. In someembodiments, module 152 may include one or more of signal requestingacquisition of adaptation data in preparation for the speech-facilitatedtransaction receiving module 222, signal requesting verification ofadaptation data in preparation for the speech-facilitated transactionreceiving module 224, signal requesting microphone activation receivingmodule 226, signal requesting opening of data port receiving module 228,and signal requesting data regarding amount of available memory spacefor speech data storage receiving module 230.

Referring again to FIG. 2, e.g., FIG. 2C, in some embodiments, module152 may include one or more of transmission of speech data by devicedetecting module 234, data regarding detected device transmitting speechdata collecting module 236 (e.g., which, in some embodiments, mayinclude one or more of data regarding location of detected devicetransmitting speech data collecting module 238 and data regarding typeof detected device transmitting speech data collecting module 240),particular party spoken speech detecting module 242 (e.g., which, insome embodiments, may include particular party spoken speech receivingusing microphone module 244), speech data comprising previously recordedparticular party speech and timestamp of recording speech receivingmodule 246, speech data comprising compressed version of data correlatedto particular party spoken words receiving module 248, and audio datacorresponding to one or more particular party spoken words receivingmodule 250.

Referring again to FIG. 2, e.g., FIG. 2D, in some embodiments, module152 may include speech data correlated to one or more particular partyspoken words receiving from a device module 252. In some embodiments,module 252 may include audio data derived from one or more particularparty spoken words receiving from a device module 254. In someembodiments, module 254 may include one or more of audio data derivedfrom one or more particular party spoken words detected by the devicereceiving from the device module 256 and audio data derived from one ormore particular party spoken words recorded by the device receiving fromthe device module 258.

Referring now to FIG. 3, FIG. 3 illustrates an exemplary implementationof adaptation data at least partly based on discrete speech interactionof particular party separate from detected speech data, and has beenstored on a particular party-associated particular device acquiringmodule 154. As illustrated in FIG. 3, the adaptation data at leastpartly based on discrete speech interaction of particular party separatefrom detected speech data, and has been stored on a particularparty-associated particular device acquiring module 154 may include oneor more sub-logic modules in various alternative implementations andembodiments. For example, as shown in FIG. 3 (e.g., FIG. 3A), in someembodiments, module 154 may include adaptation data comprising one ormore words and corresponding pronunciations of the one or more words atleast partly based on discrete speech interaction of particular partyseparate from detected speech data, and has been stored on a particularparty-associated particular device acquiring module 302. In someembodiments, module 302 may include adaptation data comprising one ormore words and corresponding pronunciations of the one or more words atleast partly based on at least one previous training by the particularparty separate from detected speech data, and has been stored on aparticular party-associated particular device acquiring module 304. Insome embodiments, module 304 may include adaptation data comprising oneor more words and corresponding pronunciations of the one or more wordsat least partly based on at least one previous training by theparticular party separate from detected speech data corresponding to anorder placed by the particular party at an automated drive-thru terminalthat accepts speech input, and has been stored on a particularparty-associated particular device acquiring module 306. In someembodiments, module 306 may include adaptation data comprising one ormore words and corresponding pronunciations of the one or more words atleast partly based on at least one previous training by the particularparty in response to cellular telephone device prompting separate fromdetected speech data corresponding to an order placed by the particularparty at an automated drive-thru terminal that accepts speech input, andhas been stored on a particular party-associated particular deviceacquiring module 308. In some embodiments, module 308 may includeadaptation data comprising one or more words and correspondingpronunciations of the one or more words at least partly based on atleast one previous training by the particular party in response tocellular telephone device prompting separate from detected speech datacorresponding to an order placed by the particular party at an automateddrive-thru terminal that accepts speech input, and has been stored on aparticular device linked to the particular party through a contract witha telecommunications provider acquiring module 310.

Referring again to FIG. 3, e.g., FIG. 3B, in some embodiments, module154 may include one or more of adaptation data at least partly based ondiscrete speech interaction of particular party at different time andlocation to speech interaction generating detected speech data, and hasbeen stored on a particular party-associated particular device acquiringmodule 312 and adaptation data at least partly based on discrete speechinteraction of particular party occurring prior to speech interactiongenerating detected speech data, and has been stored on a particularparty-associated particular device acquiring module 314. In someembodiments, module 314 may include one or more of adaptation data atleast partly based on discrete speech interaction of particular partyoccurring prior to speech interaction generating detected speech data,and has been stored on a particular party-associated particular devicereceiving from the particular device module 316 (e.g., which, in someembodiments, may include adaptation data at least partly based ondiscrete speech interaction of particular party occurring prior tospeech interaction generating detected speech data, and has been storedon a particular party-associated particular device receiving directlyfrom the particular device memory module 318) and adaptation data atleast partly based on discrete speech interaction of particular partyoccurring prior to speech interaction generating detected speech data,and has been stored on a particular party-associated particular devicereceiving from a communication network provider module 320 (e.g., which,in some embodiments, may include adaptation data at least partly basedon discrete speech interaction of particular party occurring prior tospeech interaction generating detected speech data, and has been storedon a particular party-associated particular device and transmitted overthe communication network receiving from a communication networkprovider module 322.

Referring again to FIG. 3, e.g., FIG. 3C, in some embodiments, module154 may include module 314, as previously described. In someembodiments, module 314 may include one or more of adaptation data atleast partly based on discrete speech interaction of particular partyoccurring prior to speech interaction generating detected speech data,and has been stored on a particular party-associated particular devicereceiving from a device connected to a same network as a target deviceto which the detected speech data is directed module 324 and adaptationdata at least partly based on discrete speech interaction of particularparty occurring prior to speech interaction generating detected speechdata, and has been stored on a particular party-associated particulardevice receiving in response to reception of speech data module 326. Insome embodiments, module 154 may include adaptation data at least partlybased on discrete speech interaction of particular party occurring priorto speech interaction generating detected speech data, and has beenstored on a particular party-associated particular device acquiring inresponse to condition module 328. In some embodiments, module 328 mayinclude adaptation data at least partly based on discrete speechinteraction of particular party occurring prior to speech interactiongenerating detected speech data, and has been stored on a particularparty-associated particular device acquiring in response to theparticular party interacting with a target device module 330. In someembodiments, module 330 may include one or more of adaptation data atleast partly based on discrete speech interaction of particular partyoccurring prior to speech interaction generating detected speech data,and has been stored on a particular party-associated particular deviceacquiring in response to the particular party inserting a key into amotor vehicle interacting with a target device module 332 and adaptationdata at least partly based on discrete speech interaction of particularparty occurring prior to speech interaction generating detected speechdata, and has been stored on a particular party-associated particulardevice acquiring in response to the particular party executing a programon a computing device module 334.

Referring again to FIG. 3, e.g., FIG. 3D, in some embodiments, module154 may include module 328, as previously described. In someembodiments, module 328 may include one or more of adaptation data atleast partly based on discrete speech interaction of particular partyoccurring prior to speech interaction generating detected speech data,and has been stored on a particular party-associated particular devicereceiving in response to detection of the particular party at aparticular location module 336 and adaptation data at least partly basedon discrete speech interaction of particular party occurring prior tospeech interaction generating detected speech data, and has been storedon a particular party-associated particular device receiving in responseto detection of the particular party within a particular proximity of atarget device module 338.

Referring again to FIG. 3, e.g., FIG. 3E, in some embodiments, module154 may include adaptation data at least partly based on discrete speechinteraction of particular party separate from detected speech data, andhas been stored on a particular party-associated particular deviceacquiring from a further device module 340. In some embodiments, module340 may include one or more of adaptation data originating at furtherdevice and at least partly based on discrete speech interaction ofparticular party separate from detected speech data, and has been storedon a particular party-associated particular device acquiring from afurther device module 342, adaptation data at least partly based ondiscrete speech interaction of particular party separate from detectedspeech data, and has been stored on a particular party-associatedparticular device acquiring from a further device related to theparticular device module 344, and adaptation data at least partly basedon discrete speech interaction of particular party separate fromdetected speech data, and has been stored on a particularparty-associated particular device acquiring from a further device thatreceived the adaptation data from the particular device module 352. Insome embodiments, module 344 may include one or more of adaptation dataat least partly based on discrete speech interaction of particular partyseparate from detected speech data, and has been stored on a particularparty-associated particular device acquiring from a further deviceassociated with the particular party module 346, adaptation data atleast partly based on discrete speech interaction of particular partyseparate from detected speech data, and has been stored on a particularparty-associated particular device acquiring from a further device incommunication with the particular device module 348, and adaptation dataat least partly based on discrete speech interaction of particular partyseparate from detected speech data, and has been stored on a particularparty-associated particular device acquiring from a further device atleast partially controlled by the particular device module 350.

Referring again to FIG. 3, e.g., FIG. 3F, in some embodiments, module154 may include module 340, as previously described. In someembodiments, module 340 may include adaptation data comprisinginstructions for modifying a pronunciation dictionary, said adaptationdata at least partly based on discrete speech interaction of particularparty separate from detected speech data, and has been stored on aparticular party-associated particular device acquiring from a furtherdevice module 354. In some embodiments, module 354 may includeadaptation data comprising a first instruction for modifying apronunciation dictionary based on a first particular party interactionand a second instruction for modifying a pronunciation dictionary basedon a second particular party interaction, and has been stored on aparticular party-associated particular device acquiring from a furtherdevice module 356. In some embodiments, module 356 may includeadaptation data comprising a first instruction for modifying apronunciation dictionary based on a first particular party interactionand a second instruction for modifying a pronunciation dictionary basedon a second particular party interaction, said first instruction hasbeen stored on a particular party-associated particular device acquiringfrom a further device module 358.

Referring again to FIG. 3, e.g., FIG. 3G, in some embodiments, module154 may include one or more of adaptation data at least partly based ondiscrete speech interaction of particular party separate from detectedspeech data, and has been stored on a particular party-associatedparticular device generating module 360, adaptation data at least partlybased on discrete speech interaction of particular party separate fromdetected speech data, and has been stored on a particularparty-associated particular device retrieving module 362, and adaptationdata at least partly based on discrete speech interaction of particularparty with particular type of device separate from detected speech data,and has been stored on a particular party-associated particular deviceacquiring module 364. In some embodiments, module 364 may include one ormore of adaptation data at least partly based on discrete speechinteraction of particular party with device of same type as targetdevice configured to receive speech data, said discrete interactionseparate from detected speech data, and has been stored on a particularparty-associated particular device acquiring module 366 and adaptationdata at least partly based on discrete speech interaction of particularparty with device having particular characteristic separate fromdetected speech data, and has been stored on a particularparty-associated particular device acquiring module 368. In someembodiments, module 368 may include one or more of adaptation data atleast partly based on discrete speech interaction of particular partywith device communicating on a same communication network as targetdevice and separate from detected speech data, and has been stored on aparticular party-associated particular device acquiring module 370 andadaptation data at least partly based on discrete speech interaction ofparticular party with device configured to carry out a same function asthe target device and separate from detected speech data, and has beenstored on a particular party-associated particular device acquiringmodule 372.

Referring again to FIG. 3, e.g., FIG. 3H, in some embodiments, module154 may include module 364, and module 364 may include module 368, aspreviously described. In some embodiments, module 368 may includeadaptation data at least partly based on discrete speech interaction ofparticular party with device configured to accept a same type of inputas the target device and separate from detected speech data, and hasbeen stored on a particular party-associated particular device acquiringmodule. In some embodiments, module 154 may include adaptation data atleast partly based on discrete speech interaction of particular partywith particular device separate from detected speech data, and has beenstored on a particular party-associated particular device acquiringmodule 376. In some embodiments, module 376 may include adaptation dataat least partly based on discrete speech interaction of particular partywith cellular telephone device separate from detected speech data, andhas been stored on a particular party-associated cellular telephonedevice acquiring module 378. In some embodiments, module 378 may includeone or more of adaptation data at least partly based on particular partytelephone conversation carried out using cellular telephone deviceseparate from detected speech data, and has been stored on a particularparty-associated cellular telephone acquiring module 380 and adaptationdata at least partly based on particular party speech command given tocellular telephone device separate from detected speech data, and hasbeen stored on a particular party-associated cellular telephoneacquiring module 382.

Referring again to FIG. 3, e.g., FIG. 3I, in some embodiments, module154 may include one or more of adaptation data at least partly based ondiscrete speech interaction of particular party separate from detectedspeech data and using same utterance as speech that is part of speechdata, and has been stored on a particular party-associated particulardevice acquiring module 384, adaptation data at least partly based ondiscrete speech interaction of particular party and using same utteranceas speech that is part of speech data at a different time than speechthat is part of the speech data acquiring module 386, adaptation datacomprising a phoneme dictionary based on one or more particular partypronunciations, such that at least one entry has been stored on aparticular party-associated particular device acquiring module 388,adaptation data comprising a sentence diagramming path selectionalgorithm based on one or more particular party discrete speechinteractions, and has been stored on a particular party-associatedparticular device acquiring module 390, adaptation data at least partlybased on discrete speech interaction of particular party separate fromdetected speech data, and at least partly collected by a particularparty-associated particular device acquiring module 392, and adaptationdata comprising instructions for modifying one or more portions of aspeech recognition component of a target device that are at least partlybased on one or more particular party speech interactions, and has beenstored on a particular party-associated particular device acquiringmodule 394.

Referring again to FIG. 3, e.g., FIG. 3J, in some embodiments, module154 may include one or more of adaptation data comprising a location ofinstructions for modifying one or more portions of a speech recognitioncomponent of a target device that are at least partly based on one ormore particular party speech interactions, and has been stored on aparticular party-associated particular device acquiring module 396,adaptation data at least partly based on discrete speech interaction ofparticular party separate from detected speech data, and transmittedfrom a particular party-associated particular device acquiring module398, adaptation data at least partly based on discrete speechinteraction of particular party separate from detected speech data, andstored on a particular party-associated particular device acquiringmodule 301, adaptation data at least partly based on discrete speechinteraction of particular party separate from detected speech data, andis temporarily stored on the particular-party associated particulardevice until remote server deposit acquiring module 303, and adaptationdata at least partly based on discrete speech interaction of particularparty separate from detected speech data, and was transmitted from afirst device to a second device using the particular party-associatedparticular device as a channel configured to facilitate the transactionacquiring module 305.

Referring again to FIG. 3, e.g., FIG. 3K, in some embodiments, module154 may include one or more of adaptation data at least partly based ondiscrete speech interaction of particular party separate from detectedspeech data, and at least a portion of which originated at a particularparty-associated particular device acquiring module 307, adaptation dataat least partly based on discrete speech interaction of particular partyseparate from detected speech data, and at least a portion of which wastransmitted to a remote location from a particular party-associatedparticular device receiving from remote location module 309, adaptationdata at least partly based on discrete speech interaction of particularparty separate from detected speech data receiving module 311, andfurther data adding to adaptation data module 313. In some embodiments,module 313 may include one or more of additional adaptation data addingto adaptation data module 315, header data identifying receiving entityadding to adaptation data module 317, and header data identifyingtransmitting entity adding to adaptation data module 319.

Referring now to FIG. 4, FIG. 4 illustrates an exemplary implementationof the destination of one or more of the adaptation data and the speechdata obtaining module 156. As illustrated in FIG. 4, the destination ofone or more of the adaptation data and the speech data obtaining module156 may include one or more sub-logic modules in various alternativeimplementations and embodiments. For example, as shown in FIG. 4, e.g.,FIG. 4A, in some embodiments, module 156 may include one or more of dataregarding target device configured to process speech data module 402(e.g., which, in some embodiments, may include one or more of datacomprising a target device configured to process speech data addressreceiving module 404 and data comprising a target device configured toprocess speech data location receiving module 406), target devicelocation as destination of one or more of the adaptation data and thespeech data determining module 408 (e.g., which, in some embodiments,may include target device network location as destination of one or moreof the adaptation data and the speech data determining module 470),device name of destination of one or more of the adaptation data and thespeech data obtaining module 472, type of device for which one or moreof the adaptation data and the speech data is a destination obtainingmodule 474, program component configured to perform processing on one ormore of the adaptation data and the speech data determining module 410,and program component as destination of one or more of the adaptationdata and the speech data determining module 412. In some embodiments,module 412 may include selection between application component andoperating system component as destination of one or more of theadaptation data and the speech data selecting module 414.

Referring again to FIG. 4, e.g., FIG. 4B, in some embodiments, module156 may include one or more of data regarding at least one other deviceconfigured to process detected speech data obtaining module 416 anddestination of the detected speech data determining based on acquireddata regarding at least one other device determining module 418. In someembodiments, module 416 may include one or more of at least one or moreother device configured to process detected speech data detecting module420, data regarding a number of the at least one other devicesconfigured to process detected speech data obtaining module 422, dataregarding at least one other device configured to process detectedspeech data acquiring from adaptation data module 424, detecting atleast one or more other devices configured to process detected speechdata module 426, determining whether detected speech data is intended tobe applied by one of the one or more other devices module 428, detectingone or more other devices configured to process detected speech datamodule 430, signal requesting data regarding a capability of the one ormore other devices transmitting module 432, and data regardingcapability of the one or more other devices receiving module 434.

Referring again to FIG. 4, e.g., FIG. 4C, in some embodiments, module156 may include module 416 and 418, as previously described. In someembodiments, module 416 may include one or more of one or more otherdevices configured to process detected speech data detecting module 436and capability of the detected one or more other devices configured toprocess detected speech data receiving module 438. In some embodiments,module 438 may include capability of the detected one or more otherdevices configured to process detected speech data receiving from adevice that is not one of the one or more other devices module 440. Insome embodiments, module 440 may include one or more of capability ofthe detected one or more other devices configured to process detectedspeech data receiving from a device configured to communicate on a samecommunication network as the one or more other devices module 442,capability of the detected one or more other devices configured toprocess detected speech data receiving from a device at least partiallycontrolled by a same entity that controls at least one of the one ormore other devices module 444, and capability of the detected one ormore other devices configured to process detected speech data receivingfrom a device configured to provide one or more services to at least oneof the one or more other devices module 446.

Referring again to FIG. 4, e.g., FIG. 4D, in some embodiments, module156 may include one or more of other device data regarding a capabilityof one or more other devices configured to process detected speech dataobtaining module 448 and destination for one or more of the adaptationdata and the speech data determining at least partly based on theacquired other device data module 450. In some embodiments, module 448may include one or more of other device data regarding an amount ofavailable memory for one or more detected other devices obtaining module452, other device data regarding an amount of available processorcapacity for one or more detected other devices obtaining module 454,and other device data regarding a speech data processing capability forone or more detected other devices obtaining module 456. In someembodiments, module 456 may include one or more of other device dataregarding one or more available speech models for one or more detectedother devices obtaining module 458 and other device data regarding oneor more available speech algorithms for one or more detected otherdevices obtaining module 460. In some embodiments, module 460 mayinclude other device data regarding one or more available speechalgorithms for a motor vehicle control system and a portable navigationsystem obtaining module 462 and other device data regarding an availablehidden Markov model for a motor vehicle control system and an availableconstrained maximum likelihood transformation for the portablenavigation system obtaining module 464.

Referring now to FIG. 5, FIG. 5 illustrates an exemplary implementationof the acquired destination of one or more of the adaptation data andthe speech data transmitting module 158. As illustrated in FIG. 5, theacquired destination of one or more of the adaptation data and thespeech data transmitting module 158 may include one or more sub-logicmodules in various alternative implementations and embodiments. Forexample, as shown in FIG. 5 (e.g., FIG. 5A), in some embodiments, module158 may include acquired destination of one or more of the adaptationdata and the speech data transmitting to target device module 502. Insome embodiments, module 502 may include one or more of target deviceacquired via a communication network as destination of one or more ofthe adaptation data and the speech data transmitting to target devicemodule 504 and detected speech data to target device acquired asdestination transmitting module 506. In some embodiments, module 506 mayinclude one or more of detected speech data converting into targetdevice recognizable data module 508 and converted detected speech datatransmitting to target device acquired as destination module 510. Insome embodiments, module 508 may include one or more of detected targetdevice unrecognizable speech data converting into target devicerecognizable data module 512 and detected speech data converting intodata recognizable by a target device configured to process converteddata more quickly than unconverted data module 514. In some embodiments,module 514 may include one or more of detected speech data convertinginto data recognizable by a target device configured to processconverted data more quickly than unconverted data based on a requiringconversion rule module 550 and detected speech data converting into datarecognizable by a target device configured to process converted datamore quickly than unconverted data based on target device feedbackmodule 552.

Referring again to FIG. 5, e.g., FIG. 5B, in some embodiments, module158 may include one or more of one or more filters specified by theacquired adaptation data applying to detected speech data module 516(e.g., which, in some embodiments, may include non-lexical vocableremoval filter specified by the acquired adaptation data applying todetected speech data module 520), filter-applied detected speech datatransmitting to acquired destination module 518, one or more of speechdata and adaptation data transmitting to particular memory locationmodule 524, and one or more of speech data and adaptation dataconfigured to be processed by a target device transmitting to furtherdevice module 532 (e.g., which, in some embodiments, may include one ormore of speech data and adaptation data configured to be processed by amotor vehicle control device transmitting to a personal navigationdevice module 534). In some embodiments, module 524 may include one ormore of speech data and adaptation data transmitting to target devicespeech recognition component module 526. In some embodiments, module 526may include adaptation data comprising instructions for replacing a wordfrequency table with a modified word frequency table reflectingparticular party word usage transmitting to target device speechrecognition component module 528. In some embodiments, module 528 mayinclude adaptation data comprising instructions for replacing a wordfrequency table with a modified word frequency table reflectingparticular party word usage transmitting to a motor vehicle controlsystem speech recognition component module 530.

Following are a series of flowcharts depicting implementations. For easeof understanding, the flowcharts are organized such that the initialflowcharts present implementations via an example implementation andthereafter the following flowcharts present alternate implementationsand/or expansions of the initial flowchart(s) as either sub-componentoperations or additional component operations building on one or moreearlier-presented flowcharts. Those having skill in the art willappreciate that the style of presentation utilized herein (e.g.,beginning with a presentation of a flowchart(s) presenting an exampleimplementation and thereafter providing additions to and/or furtherdetails in subsequent flowcharts) generally allows for a rapid and easyunderstanding of the various process implementations. In addition, thoseskilled in the art will further appreciate that the style ofpresentation used herein also lends itself well to modular and/orobject-oriented program design paradigms.

Further, in FIGS. 6-10 and in the figures to follow thereafter, variousoperations may be depicted in a box-within-a-box manner. Such depictionsmay indicate that an operation in an internal box may comprise anoptional example embodiment of the operational step illustrated in oneor more external boxes. However, it should be understood that internalbox operations may be viewed as independent operations separate from anyassociated external boxes and may be performed in any sequence withrespect to all other illustrated operations, or may be performedconcurrently. Still further, these operations illustrated in FIG. 6 aswell as the other operations to be described herein may be performed byat least one of a machine, an article of manufacture, or a compositionof matter.

Referring again to FIG. 6, FIG. 6 shows operation 600, which may includeoperation 602 depicting detecting speech data related to aspeech-facilitated transaction. For example, FIG. 1, e.g., FIG. 1B,shows speech data related to speech facilitated transaction detectingmodule 152 detecting (e.g., either by receiving data, or by a sensorproviding notification, e.g., a microphone of a cellular telephonedevice) speech data (e.g., audio data received from the mouth of aspeaker, or data representing speech from the mouth of a speaker)related to a speech-facilitated transaction (e.g., placing an order forhot wings and fries at an automated drive-thru window that acceptsspeech input).

Referring again to FIG. 6, operation 600 may include operation 604depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of a particular party that is discrete fromthe detected speech data, wherein at least a portion of the adaptationdata has been stored on a particular device associated with theparticular party. For example, FIG. 1, e.g., FIG. 1B, shows adaptationdata at least partly based on discrete speech interaction of particularparty separate from detected speech data, and has been stored on aparticular party-associated particular device acquiring module 154acquiring adaptation data (e.g., a set of proper noun pronunciations,e.g., food items, e.g., “Chunky's Best Wings,” or “Big Mac”) that is atleast partly based on at least one speech interaction (e.g., a previousfast food order at a similar automated drive-thru window at a Big Boyrestaurant) of a particular party (e.g., the user, sitting in her car,ordering a meal) that is discrete from the detected speech data (e.g.,the speech data of the user placing the order for hot wings and fries atthe automated drive-thru window), wherein at least a portion of theadaptation data (e.g., a set of proper noun pronunciations, e.g., fooditems, e.g., “Chunky's Best Wings,” or “Big Mac”) has been stored (e.g.,at one point was stored, if only temporarily) on a particular device(e.g., a user's cellular phone, on removable memory) associated with theparticular party (e.g., in this instance it may merely be carried by theuser and in range of the automated drive thru window, or it maybroadcast a signal indicating that the device is associated with theparty that is speaking when it detects that the user is speaking).

Referring again to FIG. 6, operation 600 may include operation 606depicting obtaining a destination of one or more of the adaptation dataand the speech data. For example, FIG. 1, e.g., FIG. 1B, showsdestination of one or more of the adaptation data and the speech dataacquiring module 156 obtaining a destination (e.g., the automateddrive-thru window) of one or more of the adaptation data and the speechdata (e.g., the automated drive thru window may be broadcasting arequest for adaptation data to help in processing the user's speech. Thecellular telephone device receives that request, thus obtaining anaddress of the automated drive-thru window which is the destination ofthe adaptation data (as well as the speech data, but in this example,the cellular device is not transmitting the speech data, but theautomated drive thru window is receiving the user's speech directly).

Referring again to FIG. 6, operation 600 may include operation 608depicting transmitting one or more of the speech data and the adaptationdata to the acquired destination. For example, FIG. 1, e.g., FIG. 1B,shows acquired destination of one or more of the adaptation data and thespeech data transmitting module 158 transmitting one or more of thespeech data and the adaptation data (e.g., in this example, theadaptation data, e.g., a set of proper noun pronunciations, e.g., fooditems, e.g., “Chunky's Best Wings,” or “Big Mac”) to the acquireddestination (e.g., the automated drive thru window).

FIGS. 7A-7D depict various implementations of operation 602, accordingto embodiments. Referring now to FIG. 7A, operation 602 may includeoperation 701 depicting detecting that a speech-facilitated transactionis occurring. For example, FIG. 2, e.g., FIG. 2A, showsspeech-facilitated transaction occurrence detecting module 201 detecting(e.g., determine the presence of, be informed of, realize, be made awareof, or otherwise learn) that a speech-facilitated transaction (e.g.,using speech to perform numeric calculations using a calculator device)is occurring (e.g., is currently taking place, or is about to takeplace, or a step that indicates the transaction is occurring or about totake place).

Referring again to FIG. 7A, operation 602 may include operation 702depicting detecting that a speech-facilitated transaction is about tooccur. For example, FIG. 2, e.g., FIG. 2A, shows speech-facilitatedtransaction about to occur detecting module 202 detecting that aspeech-facilitated transaction (e.g., setting up a playlist in a hometheater system) is about to occur (e.g., the operation of the hometheater system has pressed the “make playlist” button on a remote.”)

Referring again to FIG. 7A, operation 702 may include operation 704depicting detecting that a microphone of a device has received speech ofa speech-facilitated transaction. For example, FIG. 2, e.g., FIG. 2A,shows device microphone receiving speech of a speech-facilitatedtransaction detecting module 204 detecting that a microphone of a device(e.g., the receiver of a cellular telephone) has received speech (e.g.,the microphone sends an internal signal to the processor indicating thatit is receiving, or has received, or both, speech) of aspeech-facilitated transaction (e.g., activating a home security systemwith a particular code phrase).

Referring again to FIG. 7A, operation 602 may include operation 706depicting receiving a signal indicating that a microphone of a device isreceiving speech of a speech-facilitated transaction. For example, FIG.2, e.g., FIG. 2A, shows indicator of device microphone receiving speechof a speech facilitated transaction signal detecting module 206receiving a signal (e.g., receiving, over a network, from an externaldevice) indicating that a microphone of a device (e.g., a receiver of anaudio recording device, e.g., a personal recorder) is receiving speechof a speech-facilitated transaction (e.g., ordering a cheeseburger froman automated drive-thru machine).

Referring again to FIG. 7A, operation 706 may include operation 708depicting receiving a signal indicating that a microphone of theparticular device is receiving speech of the speech-facilitatedtransaction. For example, FIG. 2, e.g., FIG. 2A, shows indicator ofparticular device microphone receiving speech of a speech facilitatedtransaction signal detecting module 208 receiving a signal (e.g.,receiving a signal directly from a cellular telephone) indicating that amicrophone of the particular device (e.g., a user's smartphone) isreceiving speech of the speech-facilitated transaction (e.g., adictating a memorandum to a default operating system word processingsoftware loaded on a desktop computer).

Referring again to FIG. 7A, operation 706 may include operation 710depicting receiving a signal indicating that a microphone of an otherdevice is receiving speech of the speech-facilitated transaction. Forexample, FIG. 2, e.g., FIG. 2A, shows indicator of other devicemicrophone receiving speech of a speech facilitated transaction signaldetecting module 210 receiving a signal (e.g., receiving a signal from auser's cellular telephone device) indicating that a microphone of another device (e.g., a microphone of a gaming headset) is receivingspeech of the speech-facilitated transaction (e.g., issuing in-gamecommands in an online soccer video game).

Referring again to FIG. 7A, operation 706 may include operation 712depicting receiving a signal indicating that a microphone of a targetdevice configured to process the speech data is receiving speech of thespeech facilitated transaction. For example, FIG. 2, e.g., FIG. 2A,shows indicator of target device microphone receiving speech of a speechfacilitated transaction signal detecting module 212 receiving a signal(e.g., from a smartphone, e.g., which may have its microphone turned offor disabled) indicating that a microphone of a target device (e.g., areceiving microphone of an automated teller machine device) configuredto process the speech data (e.g., the automated teller machine device iscapable of processing speech data) is receiving speech of thespeech-facilitated transaction (e.g., withdrawing two hundred dollarsfrom a checking account). It is noted that, in some embodiments, thereceived signal may indicate that the speech data being received iscapable of being processed by the target device, however, in otherembodiments, the signal merely indicates that a microphone of the targetdevice is receiving speech.

Referring again to FIG. 7A, operation 602 may include operation 714depicting receiving the adaptation data. For example, FIG. 2, e.g., FIG.2A, shows adaptation data receiving module 214 receiving the adaptationdata (e.g., instructions for adapting a speech recognition component ofa target device configured to receive speech, e.g., an automated airlineticket dispenser machine).

Referring again to FIG. 7A, in embodiments in which operation 602includes operation 714, operation 602 may further include operation 716depicting determining from the reception of the adaptation data thatspeech data related to a speech-facilitated transaction is beingtransferred. For example, FIG. 2, e.g., FIG. 2A, shows reception ofadaptation data-based speech data transferring determination module 216determining from the reception of the adaptation data (e.g., theinstructions for adapting the speech recognition component) that speechdata related to a speech-facilitated transaction (e.g., printing anairline ticket) is being transferred (e.g., the system is configuredsuch that adaptation data is sent upon the starting of aspeech-facilitated transaction, so that upon receipt of the adaptationdata, it is known that speech data is being transferred).

Referring again to FIG. 7A, operation 716 may include operation 718depicting receiving the adaptation data, said adaptation data comprisingan indicator that speech data related to a speech-facilitatedtransaction is being transferred. For example, FIG. 2, e.g., FIG. 2A,shows reception of adaptation data comprising indicator based speechdata transferring determination module 218 receiving the adaptation data(e.g., data including a pronunciation dictionary and a flag indicatingthat speech is taking place), said adaptation data comprising anindicator (e.g., an electronic flag) that speech data related to aspeech-facilitated transaction (e.g., programming a speech-enabledDigital Video Recorder) is being transferred (e.g., the particular partyis speaking).

Referring now to FIG. 7B, operation 602 may include operation 720depicting receiving a signal requesting initiation of one or moreoperations in preparation for a speech-facilitated transaction. Forexample, FIG. 2, e.g., FIG. 2B, shows signal requesting initiation ofone or more speech-facilitated transaction operations receiving module220 receiving a signal (e.g., either internally or externally)requesting initiation of one or more operations (e.g., one or more ofacquiring adaptation data, reserving a portion of memory, executing oneor more commands, running a diagnostic check, activating a component,e.g., activating a microphone, adjusting a microphone sensitivity,loading a particular language set, and the like) in preparation for aspeech-facilitated transaction (e.g., giving a speech command to a motorvehicle control system).

Referring again to FIG. 7B, operation 720 may include operation 722depicting receiving a signal requesting acquisition of adaptation datain preparation for a speech-facilitated transaction. For example, FIG.2, e.g., FIG. 2B, shows signal requesting acquisition of adaptation datain preparation for the speech-facilitated transaction receiving module222 receiving a signal requesting acquisition of adaptation data (e.g.,a pronunciation dictionary) in preparation for a speech-facilitatedtransaction (e.g., an automated teller machine device transaction thatmay use numbers whose pronunciations are in the pronunciationdictionary).

Referring again to FIG. 7B, operation 720 may include operation 724depicting receiving a signal requesting verification of adaptation datain preparation for a speech-facilitated transaction. For example, FIG.2, e.g., FIG. 2B, shows signal requesting verification of adaptationdata in preparation for the speech-facilitated transaction receivingmodule 224 receiving a signal requesting verification (e.g., a requestfor a determination about adaptation data, e.g., one or more of has itbeen received, is it viable for use in the target device, is itdigitally signed and/or authenticated, and the like) of adaptation data(e.g., a stochastic state transition network) in preparation for aspeech-facilitated transaction (e.g., giving a command to increasevolume to a speech-enabled television, e.g., television 53)

Referring again to FIG. 7B, operation 720 may include operation 726depicting receiving a signal requesting activation of a microphone inpreparation to receive speech data in a form of speech. For example,FIG. 2, e.g., FIG. 2B, shows signal requesting microphone activationreceiving module 226 receiving a signal requesting activation of amicrophone in preparation to receive speech data in a form of speech(e.g., ordering a pizza with three different kinds of toppings from anautomated order-taking unit).

Referring again to FIG. 7B, operation 720 may include operation 728depicting receiving a signal requesting opening of a port in preparationto receive speech data. For example, FIG. 2, e.g., FIG. 2B, shows signalrequesting opening of data port receiving module 228 receiving a signalrequesting opening of a port (e.g., allowing access to anapplication-specific or process-specific software construct as acommunications endpoint in a computer's host operating system) inpreparation to receive speech data (e.g., data to be received at aparticular port).

Referring again to FIG. 7B, operation 720 may include operation 730depicting receiving a signal requesting data regarding how much space isavailable in memory for storage of speech data. For example, FIG. 2,e.g., FIG. 2B, shows signal requesting data regarding amount ofavailable memory space for speech data storage receiving module 230receiving a signal (e.g., receiving a message, e.g., a TCP/IP formattedmessage) requesting data regarding how much space is available in memory(e.g., memory 134) for storage of speech data (e.g., storage of anelectronic representation of words spoken by the particular party).

Referring again to FIG. 7B, operation 602 may include operation 732depicting detecting that a device is transmitting speech data. Forexample, FIG. 2, e.g., FIG. 2B, shows speech data transmission by devicedetecting module 232 detecting that a device (e.g., a cellular telephonedevice) is transmitting speech data (e.g., packets of data correspondingto broken-down speech).

Referring now to FIG. 7C, operation 602 may include operation 734depicting detecting that a device is transmitting speech data. Forexample, FIG. 2, e.g., FIG. 2C, shows transmission of speech data bydevice detecting module 234 detecting (e.g., a software monitoringapplication determines that another application on the same device istransmitting speech data) that a device (e.g., a word processingapplication of a computer) is transmitting speech data (e.g., receivedspeech converted into a transmittable data format).

Referring again to FIG. 7C, operation 602 may include operation 736depicting collecting data regarding the detected device that istransmitting speech data. For example, FIG. 2, e.g., FIG. 2C, shows dataregarding detected device transmitting speech data collecting module 236collecting data (e.g., information about one or more of an identity,location, ownership of, and one or more characteristics of the detecteddevice) that is transmitting speech data (e.g., data converted fromspeech of the particular party ordering a sandwich at an automateddrive-thru window).

Referring again to FIG. 7C, operation 736 may include operation 738depicting collecting location data regarding the detected device that istransmitting speech data. For example, FIG. 2, e.g., FIG. 2C, shows dataregarding location of detected device transmitting speech datacollecting module 238 collecting location data (e.g., a position of thedevice, or its address on a network) regarding the detected device(e.g., a copying machine on a corporate enterprise network) that istransmitting speech data (packetized data representing a user givingcommands, e.g., a user giving sizing and color palette instructions tothe copier using speech).

Referring again to FIG. 7C, operation 736 may include operation 740depicting collecting information regarding a type of the detected devicethat is transmitting speech data. For example, FIG. 2, e.g., FIG. 2C,shows data regarding type of detected device transmitting speech datacollecting module 240 collecting information (e.g., identificationinformation, including any kind of type information, including but notlimited to manufacturer information, serial number, MAC address, otheridentifier, class of device, radio capacity, and the like) regarding atype of the detected device (e.g., whether the detected device is acellular telephone, or a pair of customized eyeglasses that areconfigured to pick up speech of the wearer) that is transmitting speechdata).

Referring again to FIG. 7C, operation 602 may include operation 742depicting detecting speech that is spoken by the particular party. Forexample, FIG. 2, e.g., FIG. 2C, shows particular party spoken speechdetecting module 242 detecting speech (e.g., a request to withdraw moneyfrom a speech-enabled automated teller machine device) that is spoken bythe particular party (e.g., a user attempting to withdraw money).

Referring again to FIG. 7C, operation 742 may include operation 744depicting receiving, using a microphone, speech that is spoken by theparticular party. For example, FIG. 2, e.g., FIG. 2C, shows particularparty spoken speech receiving using microphone module 244 receiving,using a microphone (e.g., a microphone of a cellular telephone device),speech that is spoken by the particular party (e.g., speaking the nameof a destination city at an automated airline ticket dispenser).

Referring again to FIG. 7C, operation 602 may include operation 746depicting receiving speech data comprising previously recorded speech bythe particular party, and a timestamp corresponding to a time at whichthe speech data was recorded. For example, FIG. 2, e.g., FIG. 2C, showsspeech data comprising previously recorded particular party speech andtimestamp of recording speech receiving module 246 receiving speech data(e.g., an MPEG-2 Audio Layer III (e.g., “MP3”) formatted data file)comprising previously recorded speech by the particular party (e.g.,speech dictating a memorandum), and a timestamp (e.g., a file with thetime in plaintext) corresponding to a time at which the speech data wasrecorded.

Referring again to FIG. 7C, operation 602 may include operation 748depicting receiving speech data that comprises a compressed version ofdata correlated to one or more words spoken by the particular party. Forexample, FIG. 2, e.g., FIG. 2C, shows speech data comprising compressedversion of data correlated to particular party spoken words receivingmodule 248 receiving speech data (e.g., data corresponding to speech ofthe particular party ordering a cheeseburger at an automated drive-thruwindow) that comprises a compressed version of data (e.g., Code ExcitedLinear Prediction “CELP” coding) correlated to one or more words (e.g.,“two bacon cheeseburger combos, please”) spoken by the particular party(e.g., the user ordering food).

Referring again to FIG. 7C, operation 602 may include operation 750depicting receiving audio data corresponding to one or more words spokenby the particular party. For example, FIG. 2, e.g., FIG. 2C, shows audiodata corresponding to one or more particular party spoken wordsreceiving module 250 receiving audio data (e.g., a .wav-formatted filetransmitted in packets) corresponding to one or more words spoken by theparticular party (e.g., a user giving a command to a computer that maynot be speech-enabled).

Referring now to FIG. 7D, operation 602 may include operation 752depicting receiving, from a device, speech data correlated to one ormore words spoken by a particular party. For example, FIG. 2, e.g., FIG.2D, shows speech data correlated to one or more particular party spokenwords receiving from a device module 252 receiving, from a device (e.g.,a USB storage device that also can transmit and receive data) speechdata (e.g., a text file transcript of words spoken by a user) correlatedto one or more words spoken by a particular party (e.g., words settingup a recording of the Notre Dame football game on Saturday).

Referring again to FIG. 7D, operation 752 may include operation 754depicting receiving, from the device, audio data derived from one ormore words spoken by the particular party. For example, FIG. 2, e.g.,FIG. 2D, shows audio data derived from one or more particular partyspoken words receiving from a device module 252 receiving, from thedevice (e.g., receiving, from a tablet device, which in turn receivedfrom a cellular telephone device) audio data (e.g., altered audio data,e.g., audio data with identifiable speaker characteristics removed)derived from (e.g., based on, or that used as a starting point of thealgorithm) one or more words spoken by the particular party (e.g., wordsselecting a destination from an automated airline ticket dispenser).

Referring again to FIG. 7D, operation 754 may include operation 756depicting receiving, from the device, audio data derived from one ormore words spoken by the particular party and detected by the device.For example, FIG. 2, e.g., FIG. 2D, shows audio data derived from one ormore particular party spoken words detected by the device receiving fromthe device module 256 receiving, from the device (e.g., from aninternet-enabled personal voice recorder (PVR)), audio data (e.g.,noise-filtered speech data) derived from one or more words spoken by theparticular party (e.g., dictation of a grocery list) and detected by thedevice (e.g., the PVR).

Referring again to FIG. 7D, operation 754 may include operation 758depicting receiving, from the device, audio data derived from one ormore words spoken by the particular party and recorded by the device.For example, FIG. 2, e.g., FIG. 2D, shows audio data derived from one ormore particular party spoken words recorded by the device receiving fromthe device module 258 receiving, from the device (e.g., from asmartphone), audio data (e.g., compressed and packetized audio data)derived from one or more words spoken by the particular party (e.g.,commanding a speech-enabled television to tune to a specific televisionshow when the channel is unknown).

FIGS. 8A-8Q depict various implementations of operation 604, accordingto embodiments. Referring now to FIG. 8A, operation 604 may includeoperation 802 depicting acquiring data comprising one or more words andcorresponding pronunciations of the one or more words that is at leastpartly based on at least one speech interaction of the particular party,said at least one speech interaction of the particular party discretefrom the detected speech data, wherein at least a portion of theadaptation data has been stored on the particular device associated withthe particular party. For example, FIG. 3, e.g., FIG. 3A, showsadaptation data comprising one or more words and correspondingpronunciations of the one or more words at least partly based ondiscrete speech interaction of particular party separate from detectedspeech data, and has been stored on a particular party-associatedparticular device acquiring module 302 acquiring data comprising one ormore words (e.g., “pepperoni,” “cheese,” and “anchovies”) andcorresponding pronunciations of the one or more words that is at leastpartly based on one speech interaction of the particular party (e.g.,using a cellular telephone device to order a pizza), said at least onespeech interaction of the particular party discrete from the detectedspeech data (e.g., the user is placing an order at an automateddrive-thru window), wherein at least a portion of the adaptation datahas been stored on the particular device (e.g., the cellular telephoneused to order the pizza) associated with the particular party (e.g.,owned by the user).

Referring again to FIG. 8A, operation 802 may include operation 804depicting acquiring data comprising one or more words and correspondingpronunciations of the one or more words that is at least partly based onat least one previous training by the particular party providing thepronunciations of the one or more words in response to prompting, thatis discrete from the detected speech data, wherein at least a portion ofthe adaptation data has been stored on the particular device associatedwith the particular party. For example, FIG. 3, e.g., FIG. 3A, showsadaptation data comprising one or more words and correspondingpronunciations of the one or more words at least partly based on atleast one previous training by the particular party separate fromdetected speech data, and has been stored on a particularparty-associated particular device acquiring module 304 acquiring datacomprising one or more words (e.g., “Boston,” “Austin,” and “flossed”)and corresponding pronunciations of the one or more words that is atleast partly based on at least one previous training by the particularparty providing the pronunciations of the one or more words in responseto prompting (e.g., displaying on a computer screen), that is discretefrom the detected speech data (e.g., data used in a transaction ofbuying a train ticket from a speech-enabled automated ticket dispenser),wherein at least a portion of the adaptation data has been stored on theparticular device (e.g., a USB device that can also transmit andreceive, that was previously inserted into the computer during or afterthe user's training, and is now carried by the user) associated with theparticular party (e.g., the USB device is a necklace, wristband, watch,or pair of eyeglasses that the user is wearing).

Referring again to FIG. 8A, operation 804 may include operation 806depicting acquiring adaptation data comprising one or more words andcorresponding pronunciations of the one or more words that is at leastpartly based on at least one previous training by the particular partyrepeating the pronunciations of the one or more words in response toprompting by the particular device, that is discrete from the detectedspeech data corresponding to an order placed by the particular party atan automated drive-thru terminal that accepts speech input, wherein atleast a portion of the adaptation data has been stored on the particulardevice associated with the particular party. For example, FIG. 3, e.g.,FIG. 3, shows adaptation data comprising one or more words andcorresponding pronunciations of the one or more words at least partlybased on at least one previous training by the particular party separatefrom detected speech data corresponding to an order placed by theparticular party at an automated drive-thru terminal that accepts speechinput, and has been stored on a particular party-associated particulardevice acquiring module 306 acquiring adaptation data comprising one ormore words (e.g., “national,” “first,” “bank,” “money,” and “personalidentification number”) and corresponding pronunciations of the one ormore words that is at least partly based on at least one previoustraining by the particular party repeating the pronunciations of the oneor more words in response to prompting by the particular device (e.g., acustom headset that the user wears and which provides audio prompting tothe user through the earphone portion of the headset), that is discretefrom the detected speech data corresponding to an order placed by theparticular party at an automated drive-thru terminal that accepts speechinput, wherein at least a portion of the adaptation data has been storedon the particular device (e.g., the training data was briefly stored atthe headset and then transferred to a location within a cloud network)associated with the particular party (e.g., used by the user at onepoint previously).

Referring again to FIG. 8A, operation 806 may include operation 808depicting acquiring adaptation data comprising one or more words andcorresponding pronunciations of the one or more words that is at leastpartly based on at least one previous training by the particular partyrepeating the pronunciations of the one or more words in response toprompting by a cellular telephone device with a screen and a memory,that is discrete from the detected speech data corresponding to an orderfor food placed by the particular party at an automated drive-thruterminal that accepts speech input, wherein at least a portion of theadaptation data has been stored on the cellular telephone deviceassociated with the particular party. For example, FIG. 3, e.g., FIG.3A, shows adaptation data comprising one or more words and correspondingpronunciations of the one or more words at least partly based on atleast one previous training by the particular party in response tocellular telephone device prompting separate from detected speech datacorresponding to an order placed by the particular party at an automateddrive-thru terminal that accepts speech input, and has been stored on aparticular party-associated particular device acquiring module 308acquiring adaptation data comprising one or more words (e.g.,“cheeseburger,” “small,” “medium,” and “large”) and correspondingpronunciations of the one or more words that is at least partly based onat least one previous training by the particular party repeating thepronunciations of the one or more words in response to prompting by acellular telephone device with a screen (e.g., user interface 135) and amemory (e.g., memory 134), that is discrete from the detected speechdata corresponding to an order for food placed by the particular partyat an automated drive-thru terminal that accepts speech input, whereinat least a portion of the adaptation data has been stored on thecellular telephone device associated with the particular party.

Referring now to FIG. 8B, operation 808 (e.g., operations 804, 806, and808 have been abbreviated for clarity, but are the same as in FIG. 8A)may include operation 810 depicting acquiring adaptation data comprisingone or more words and corresponding pronunciations of the one or morewords that is at least partly based on at least one previous training bythe particular party repeating the pronunciations of the one or morewords in response to prompting by a cellular telephone device with ascreen and a memory, that is discrete from the detected speech datacorresponding to an order for food placed by the particular party at anautomated drive-thru terminal that accepts speech input, wherein atleast a portion of the adaptation data has been stored on the cellulartelephone device that is linked to the particular party through acontract with a telecommunications provider. For example, FIG. 3, e.g.,FIG. 3A, shows adaptation data comprising one or more words andcorresponding pronunciations of the one or more words at least partlybased on at least one previous training by the particular party inresponse to cellular telephone device prompting separate from detectedspeech data corresponding to an order placed by the particular party atan automated drive-thru terminal that accepts speech input, and has beenstored on a particular device linked to the particular party through acontract with a telecommunications provider acquiring module 310comprising one or more words and corresponding pronunciations (e.g.,“money,” “yes,” “no,” and “please repeat that”) of the one or more wordsthat is at least partly based on at least one previous training by theparticular party repeating the pronunciations of the one or more wordsin response to prompting by a cellular telephone device with a screen(e.g., user interface 135) and a memory (e.g., memory 134),

Referring now to FIG. 8C, operation 604 may include operation 812depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party that occurred thatoccurred at a different time and a different location than a speechinteraction prior to a speech interaction that generated the detectedspeech data, wherein at least a portion of the adaptation data has beenstored on the particular device associated with the particular party.For example, FIG. 3, e.g., FIG. 3B, shows adaptation data at leastpartly based on discrete speech interaction of particular party prior tospeech interaction generating detected speech data, and has been storedon a particular party-associated particular device receiving module 312acquiring adaptation data (e.g., a noise level dependent filtrationalgorithm) that is at least partly based on at least one speechinteraction (e.g., giving speech commands to an automated teller machinedevice at a Jun. 20, 2011 baseball game in Washington, D.C.) of theparticular party that occurred at a different time (e.g., Jun. 20, 2011)and a different location (e.g., Washington, D.C.) than a speechinteraction prior to a speech interaction that generated the speechadaptation data (e.g., using an automated teller machine at a KISSconcert in Philadelphia, Pa., on Nov. 4, 2011), wherein at least aportion of the adaptation data has been stored on a particular deviceassociated with the particular party (e.g., the adaptation data, whichusually resides in cloud storage, was transmitted to the user's cellulartelephone device, then transmitted to the automated teller machinedevice).

Referring again to FIG. 8C, operation 604 may include operation 814depicting acquiring at least a portion of adaptation data that is atleast partly based on at least one speech interaction of the particularparty that occurred prior to a speech interaction that generated thedetected speech data, wherein at least a portion of the adaptation datahas been stored on the particular device associated with the particularparty. For example, FIG. 3, e.g., FIG. 3B, shows adaptation data atleast partly based on discrete speech interaction of particular partyoccurring prior to speech interaction generating detected speech data,and has been stored on a particular party-associated particular deviceacquiring module 314 acquiring at least a portion of adaptation data(e.g., an emotion-based pronunciation adjustment algorithm) that is atleast partly based on at least one speech interaction of the particularparty (e.g., programming a speech-operated microwave oven) that occurredprior to a speech interaction that generated the detected speech data(e.g., programming a PVR to record the “30 Rock” television show),wherein at least a portion of the adaptation data has been stored on aparticular device (e.g., in a hard drive on a home computer that isnetworked to other devices in the house) associated with the particularparty (e.g., the home computer is configured to manage the adaptationdata for the particular party and to transmit it to personal devicesand/or to target devices).

Referring again to FIG. 8C, operation 814 may include operation 816depicting receiving, from the particular device, adaptation data that isat least partly based on at least one speech interaction of theparticular party that occurred prior to a speech interaction thatgenerated the detected speech data, wherein at least a portion of theadaptation data has been stored on the particular device associated withthe particular party. For example, FIG. 3, e.g., FIG. 3B, showsadaptation data at least partly based on discrete speech interaction ofparticular party occurring prior to speech interaction generatingdetected speech data, and has been stored on a particularparty-associated particular device receiving from the particular devicemodule 316 receiving (e.g., a cellular telephone device, e.g., aniPhone, carried by a user, receives), from the particular device (e.g.,a programmable universal remote control) adaptation data (e.g., asyllable pronunciation database) that is at least partly based on atleast one speech interaction of the particular party (e.g., using speechto enter in “ESPN” and “Comedy Central” as favorite networks into thecable box) that occurred prior to a speech interaction that generatedthe detected speech data (e.g., the user using speech to command atelevision to move to a particular channel, e.g., ESPN-2), wherein atleast a portion of the adaptation data has been stored on a particulardevice associated with the particular party (e.g., at least a portion ofthe syllable pronunciation database) has been stored on the particulardevice associated with the particular party (e.g., the universal remotecontrol, which has been programmed by the user, and that is configuredto store at least a portion of adaptation data).

Referring again to FIG. 8C, operation 816 may include operation 818depicting receiving, from a memory of the particular device, adaptationdata that is at least partly based on at least one speech interaction ofthe particular party that occurred prior to a speech interaction thatgenerated the detected speech data, wherein the adaptation data has beenstored on the particular device associated with the particular party.For example, FIG. 3, e.g., FIG. 3B, shows adaptation data at leastpartly based on discrete speech interaction of particular partyoccurring prior to speech interaction generating detected speech data,and has been stored on a particular party-associated particular devicereceiving directly from the particular device memory module 318receiving (e.g., a CPU of a tablet device, e.g., an Asus A500 internallyreceiving from a bus connected to the processor), from a memory of theparticular device (e.g., which may be removable memory, e.g., an SD orMicro SD card) or fixed memory (e.g., internal device RAM), adaptationdata (e.g., an accent-based pronunciation modification algorithm) thatis at least partly based on at least one speech interaction of theparticular party (e.g., the user, when driving his Honda Civic motorvehicle commanding that the windows be lowered) that occurred prior to aspeech interaction that generated the detected speech data (e.g., afterthe user trades in a Honda Civic motor vehicle for an Acura TL motorvehicle, the user commands the Acura TL to lower the windows), whereinthe adaptation data has been stored on a particular device (e.g., thetablet device, e.g., the Asus A500) associated with the particular party(e.g., is known by the vehicle as associated with a particular party).

Referring now to FIG. 8D, operation 814 may include operation 820depicting receiving, from a communication network provider, adaptationdata that is at least partly based on at least one speech interaction ofthe particular party that occurred prior to a speech interaction thatgenerated the detected speech data, wherein at least a portion of theadaptation data has been stored on the particular device associated withthe particular party. For example, FIG. 3, e.g., FIG. 3B, showsadaptation data at least partly based on discrete speech interaction ofparticular party occurring prior to speech interaction generatingdetected speech data, and has been stored on a particularparty-associated particular device receiving from a communicationnetwork provider module 320 receiving (e.g., a cellular telephonedevice), from a communication provider (e.g., a provider for thecellular telephone device, e.g., AT&T), adaptation data (e.g.,instructions for replacing a word frequency table with a modified wordfrequency table that reflects the particular party's word usage) that isat least partly based on at least one speech interaction of theparticular party (e.g., a command given to the cellular phone device of“update calendar to add Mrs. Jones's birthday party on July 19th at 8pm”) that occurred prior to a speech interaction that generated thedetected speech data (e.g., a command given to an automated ticketdispensing machine), wherein at least a portion of the adaptation datahas been stored on a particular device (e.g., data storing the wordfrequency of the interactions with the cellular phone device (e.g., oneusage each of the words “calendar,” “July,” “birthday,” party,”“nineteenth” and “8 pm”) is stored on the cellular telephone devicebefore sending to the communication network provider for aggregationinto the modified word frequency table and/or conversion intoinstructions for replacing the word frequency table with the modifiedword frequency table).

Referring again to FIG. 8D, operation 820 may include operation 822depicting receiving, from a communication network provider, adaptationdata that is at least partly based on at least one speech interaction ofthe particular party that occurred prior to a speech interaction thatgenerated the detected speech data, wherein at least a portion of theadaptation data has been stored on the particular device associated withthe particular party and previously transmitted to the communicationnetwork provider. For example, FIG. 3, e.g., FIG. 3B, shows adaptationdata at least partly based on discrete speech interaction of particularparty occurring prior to speech interaction generating detected speechdata, and has been stored on a particular party-associated particulardevice and transmitted over the communication network receiving from acommunication network provider module 322 receiving, from acommunication network provider (e.g., AT&T), adaptation data (e.g., aphoneme pronunciation database) that is at least partly based on atleast one speech interaction of the particular party (e.g., placing afood order at an automated walk-thru window (e.g., similar to adrive-thru window, except you walk or conveyor belt ride through)) thatoccurred prior to a speech interaction that generated the detectedspeech data (e.g., withdrawing money from a speech-enabled automatedteller machine device), wherein at least a portion of the adaptationdata (e.g., the phoneme pronunciation database) has been stored on theparticular device associated with the particular party and previouslytransmitted to the communication network provider.

Referring again to FIG. 8D, operation 814 may include operation 824depicting receiving adaptation data, from a device connected to a samenetwork as a target device to which the detected speech data isdirected, said adaptation data at least partly based on at least onespeech interaction of the particular party that occurred prior to aspeech interaction that generated the detected speech data, wherein atleast a portion of the adaptation data has been stored on the particulardevice associated with the particular party. For example, FIG. 3, e.g.,FIG. 3C, shows adaptation data at least partly based on discrete speechinteraction of particular party occurring prior to speech interactiongenerating detected speech data, and has been stored on a particularparty-associated particular device receiving from a device connected toa same network as a target device to which the detected speech data isdirected module 324 receiving adaptation data (e.g., a stochastic statetransition network), from a device connected to a same network (e.g., atablet device connected to a home network via a router) as a targetdevice (e.g., a safe in a home that responds to speech commands and isconnected to the home network) to which the detected speech data isdirected (e.g., it is determined, e.g., by the tablet, that the detectedspeech is intended for the tablet device), said adaptation data at leastpartly based on at least one speech interaction of the particular party(e.g., the user's previous interaction with other portions of the homesecurity system, and the user's previous interactions with a speech- andnetwork-enabled coffee maker) that occurred prior to a speechinteraction that generated the detected speech data (e.g., the userprogramming the safe with the code phrase that will unlock one sectionof the safe), wherein at least a portion of the adaptation data has beenstored on the particular device (e.g., the tablet device) associatedwith the particular party (e.g., owned by the user).

Referring again to FIG. 8D, operation 814 may include operation 826depicting retrieving adaptation data in response to reception of thespeech data, said adaptation data at least partly based on at least onespeech interaction of the particular party that occurred prior to aspeech interaction that generated the detected speech data, wherein atleast a portion of the adaptation data has been stored on the particulardevice associated with the particular party. For example, FIG. 3, e.g.,FIG. 3C, shows adaptation data at least partly based on discrete speechinteraction of particular party occurring prior to speech interactiongenerating detected speech data, and has been stored on a particularparty-associated particular device receiving in response to reception ofspeech data module 326 retrieving adaptation data (e.g., an officeassistant device carried by employees (e.g., that might double as asecurity badge/access card for certain areas) receives adaptation datawhen it receives the speech data from the user) in response to receptionof the speech data (e.g., in response to the user speaking a command toa piece of office equipment, e.g., a copier, a vending machine, or anautomated security checkpoint), said adaptation data (e.g., a speechdisfluency detection algorithm) at least partly based on at least onespeech interaction of the particular party (e.g., training of theparticular party's speech that happened at the beginning of heremployment, e.g., at new employee orientation) that occurred prior to aspeech interaction that generated the detected speech data (e.g.,speaking a particular code phrase to an additional security lock toaccess a limited-access portion of a company, e.g., a document retentionroom where confidential, protected, or limited access, e.g., medical,records are kept), wherein at least a portion of the adaptation data(e.g., a speech disfluency detection algorithm) has been stored on theparticular device (e.g., the office assistant device) associated withthe particular party.

Referring now to FIG. 8E, operation 604 may include operation 828depicting acquiring adaptation data in response to a detection of aparticular condition, said adaptation data at least partly based on atleast one speech interaction of the particular party that occurred priorto a speech interaction that generated the detected speech data, whereinat least a portion of the adaptation data has been stored on theparticular device associated with the particular party. For example,FIG. 3, e.g., FIG. 3C, shows adaptation data at least partly based ondiscrete speech interaction of particular party occurring prior tospeech interaction generating detected speech data, and has been storedon a particular party-associated particular device receiving in responseto condition module 320 acquiring adaptation data (e.g., retrieving,from a cloud storage service, a context-based repaired utteranceprocessing matrix) in response to a detection of a particular condition(e.g., in response to detecting a broadcasting signal being sent from atarget device indicating that the target device (e.g., an automated fastfood drive-thru window) is configured to receive adaptation data and usethe adaptation data in speech processing), said adaptation data at leastpartly based on at least one speech interaction of the particular partythat occurred prior to a speech interaction that generated the detectedspeech data (e.g., the particular party ordering a #6 combo meal at apopular fast food restaurant), wherein at least a portion of theparticular data has been stored on the particular device associated withthe particular party (e.g., at times when the particular party requeststhe adaptation data from the cloud storage service, it is temporarilystored on the particular device before being passed along to the targetdevice).

Referring again to FIG. 8E, operation 828 may include operation 830depicting acquiring adaptation data in response to the particular partyinteracting with a target device to which the speech data is directed,said adaptation data at least partly based on at least one speechinteraction of the particular party that occurred prior to a speechinteraction that generated the detected speech data, wherein at least aportion of the adaptation data has been stored on the particular deviceassociated with the particular party. For example, FIG. 3, e.g., FIG.3C, shows adaptation data at least partly based on discrete speechinteraction of particular party occurring prior to speech interactiongenerating detected speech data, and has been stored on a particularparty-associated particular device receiving in response to theparticular party interacting with a target device module 330 acquiringadaptation data (e.g., a non-lexical vocable removal algorithm) inresponse to the particular party interacting (e.g., pushing a button on)with a target device (e.g., a speech-enabled automated teller machinedevice) to which the speech data is directed (e.g., the user is speakingto the speech-enabled automated teller machine device), said adaptationdata at least partly based on at least one speech interaction of theparticular party (e.g., one or more previous interactions with otherautomated teller machine devices) that occurred prior to a speechinteraction that generated the detected speech data (e.g., the usercommanding the automated teller machine device to dispense two hundreddollars in cash from the user's savings account), wherein at least aportion of the adaptation data has been stored on the particular device(e.g., transmit, store, and receive-enabled eyeglasses) associated withthe particular party (e.g., being worn by the user).

Referring again to FIG. 8E, operation 830 may include operation 832depicting acquiring adaptation data in response to the particular partyinserting a key into a motor vehicle to which the speech data isdirected, said adaptation data at least partly based on at least onespeech interaction of the particular party that occurred prior to aspeech interaction that generated the detected speech data, wherein atleast a portion of the adaptation data has been stored on the particulardevice associated with the particular party. For example, FIG. 3, e.g.,FIG. 3C, shows adaptation data at least partly based on discrete speechinteraction of particular party occurring prior to speech interactiongenerating detected speech data, and has been stored on a particularparty-associated particular device receiving in response to theparticular party inserting a key into a motor vehicle interacting with atarget device module 332 acquiring adaptation data (e.g., a set ofproper noun pronunciations, e.g., names of hamburger joints) in responseto the particular party inserting a key into a motor vehicle to whichthe speech data is directed (e.g., the speech data is a command “give medirections to Beastly Burger hamburger joint”), wherein at least aportion of the adaptation data has been stored on the particular device(e.g., the particular device could be the key itself, if the key isconfigured to store, transmit, and receive data, or the particulardevice could be the user's smartphone, e.g., the particular device doesnot necessarily need to be the device (e.g., the key) that triggers theacquisition of adaptation data).

Referring again to FIG. 8E, operation 830 may include operation 834depicting acquiring adaptation data in response to the particular partyexecuting a program on a computing device to which the speech data isdirected, said adaptation data at least partly based on at least onespeech interaction of the particular party that occurred prior to aspeech interaction that generated the detected speech data, wherein atleast a portion of the adaptation data has been stored on the particulardevice associated with the particular party. For example, FIG. 3, e.g.,FIG. 3D, shows adaptation data at least partly based on discrete speechinteraction of particular party occurring prior to speech interactiongenerating detected speech data, and has been stored on a particularparty-associated particular device receiving in response to theparticular party executing a program on a computing device module 334acquiring adaptation data (e.g., a part-of-speech labeling algorithm) inresponse to the particular party executing a program on a computingdevice (e.g., a word processing program) to which speech data isdirected (e.g., that is configured to receive dictation of documents),said adaptation data at least partly based on at least one speechinteraction of the particular party (e.g., previous dictations ofdocuments into a different word processing program on a differentcomputer) that occurred prior to a speech interaction that generated thedetected speech data (e.g., the speech data that will be generated bythe user's dictation), wherein at least a portion of the adaptation datahas been stored on the particular device (e.g., a USB key that is ownedby the user and that stores her adaptation data along with otherinformation) associated with the particular party (e.g., owned by theuser).

Referring now to FIG. 8F, operation 828 may include operation 836depicting acquiring adaptation data in response to a detection of theparticular party at a particular location, said adaptation data at leastpartly based on at least one speech interaction of the particular partythat occurred prior to a speech interaction that generated the detectedspeech data, wherein at least a portion of the adaptation data has beenstored on the particular device associated with the particular party.For example, FIG. 3, e.g., FIG. 3D shows adaptation data at least partlybased on discrete speech interaction of particular party occurring priorto speech interaction generating detected speech data, and has beenstored on a particular party-associated particular device receiving inresponse to detection of the particular party at a particular locationmodule 336 acquiring adaptation data in response to a detection of theparticular party at a particular location (e.g., within two feet of atarget device, e.g., an automated airline ticket dispensing counter),said adaptation data at least partly based on at least one speechinteraction of the particular party that occurred prior to a speechinteraction that generated the detected speech data (e.g., speaking thename of the destination of the user's airline ticket), wherein at leasta portion of the adaptation data (e.g., a French language substitutionalgorithm) has been stored on the particular device (e.g., a smartphonewith GPS sensors) associated with the particular party (e.g., carried bythe user).

Referring again to FIG. 8F, operation 828 may include operation 838depicting acquiring adaptation data in response to a detection of theparticular party within a particular proximity of a target device, saidadaptation data at least partly based on at least one speech interactionof the particular party that occurred prior to a speech interaction thatgenerated the detected speech data, wherein at least a portion of theadaptation data has been stored on the particular device associated withthe particular party. For example, FIG. 3, e.g., FIG. 3D, showsadaptation data at least partly based on discrete speech interaction ofparticular party occurring prior to speech interaction generatingdetected speech data, and has been stored on a particularparty-associated particular device receiving in response to detection ofthe particular party within a particular proximity of a target devicemodule 338 acquiring adaptation data (e.g., an utterance ignoringalgorithm) in response to a detection of the particular party (e.g., theuser) within a particular proximity of a target device (e.g., theparticular device acquires the adaptation data from a cloud storageservice when it receives a signal from the target device that the targetdevice (e.g., an automated drink dispensing device) detected theparticular party was within screen-viewing distance of the automateddrive-thru window), said adaptation data at least partly based on atleast one speech interaction of the particular party (e.g., theparticular party dictating a memorandum to speech-enabled wordprocessing software that is stored on a cloud) that occurred prior to aspeech interaction that generated the detected speech data (e.g.,ordering a cherry-and-chocolate twisted lime soda drink), wherein atleast a portion of the adaptation data has been stored on the particulardevice (e.g., a “smart wallet” that, in addition to holding cash andcredit cards, also can store, transmit, and receive adaptation data, andthat acquires the adaptation data when it learns that a particular partyis within proximity to a particular type of target device) associatedwith the particular party (e.g., carried by the particular party andconfigured to store, at least temporarily, the particular party'sadaptation data).

Referring now to FIG. 8G, operation 604 may include operation 840depicting acquiring adaptation data from a further device, saidadaptation data at least partly based on at least one speech interactionof the particular party that is discrete from the detected speech data,wherein at least a portion of the adaptation data has been stored on theparticular device associated with the particular party. For example,FIG. 3, e.g., FIG. 3E, shows adaptation data at least partly based ondiscrete speech interaction of particular party separate from detectedspeech data, and has been stored on a particular party-associatedparticular device acquiring from a further device module 340 acquiringadaptation data, from a further device (e.g., from a cellular telephonedevice), said adaptation data at least partly based on at least onespeech interaction of the particular party (e.g., previous commandsgiven to a navigation device requesting directions) that is discretefrom the detected speech data (e.g., requesting directions to Big BoyPizza), wherein at least a portion of the adaptation data has beenstored on the particular device (e.g., a smart key inserted into avehicle that can store, transmit, and receive adaptation data)associated with the particular party (e.g., the driver of a car that hasboth onboard navigation and a personal GPS navigation system removablymounted to the windshield).

Referring again to FIG. 8G, operation 840 may include operation 842depicting acquiring adaptation data from a further device, saidadaptation data originating at the further device and at least partlybased on least one speech interaction of the particular party that isdiscrete from the detected speech data, wherein at least a portion ofthe adaptation data has been stored on the particular device associatedwith the particular party. For example, FIG. 3, e.g., FIG. 3E, showsadaptation data originating at further device and at least partly basedon discrete speech interaction of particular party separate fromdetected speech data, and has been stored on a particularparty-associated particular device acquiring from a further devicemodule 342 acquiring adaptation data from a further device (e.g., anoffice personal device, which may be owned by the company that the userworks for, and stores at least a portion, or a version of the adaptationdata), said adaptation data originating at the further device (e.g., theadaptation data is stored on the further device once and thentransmitted from there; e.g., the further device does not receive theadaptation data from another source on demand) and at least partly basedon at least one speech interaction of the particular party that isdiscrete from the detected speech data (e.g., operating a piece ofmachinery used in that field that responds to speech commands), whereinat least a portion of the adaptation data has been stored on theparticular device associated with the particular party (e.g., theadaptation data is transferred from a further device to a particulardevice (e.g., the user's cellular telephone, which may performadditional modifications, or may transmit it as is to the target device,e.g., the piece of machinery).

Referring again to FIG. 8G, operation 840 may include operation 844depicting acquiring adaptation data from a further device related to theparticular device, said adaptation data originating at the furtherdevice and at least partly based on least one speech interaction of theparticular party that is discrete from the detected speech data, whereinat least a portion of the adaptation data has been stored on theparticular device associated with the particular party. For example,FIG. 3, e.g., FIG. 3E, shows adaptation data at least partly based ondiscrete speech interaction of particular party separate from detectedspeech data, and has been stored on a particular party-associatedparticular device acquiring from a further device related to theparticular device module 844 acquiring adaptation data from a furtherdevice (e.g., a desktop computer that stores adaptation data for a user,e.g., or for the user's entire family) related to (e.g., both theparticular device and the further device have a login saved for theuser) the particular device (e.g., a cellular telephone device), saidadaptation data originating at the further device (e.g., the adaptationdata is stored at the further device and transmitted to the particulardevice over a network, e.g., a Wi-Fi network) and at least partly basedon at least one speech interaction of the particular party that isdiscrete from the detected speech data (e.g., speech-programming aconvection oven, wherein the convection oven isn't connected by Wi-Fibut does have a Bluetooth connection and the cellular telephone device,as the particular device, acquires the adaptation data from the desktopcomputer via Wi-Fi, and relays the adaptation data to the convectionoven via Bluetooth), wherein at least a portion of the adaptation datahas been stored on the particular device (e.g., the adaptation data isstored on the cellular telephone device, at least temporarily, as it isreceived over Wi-Fi and transmitted over Bluetooth) associated with theparticular party.

Referring again to FIG. 8G, operation 844 may include operation 846depicting acquiring adaptation data from a further device associatedwith the particular party, said adaptation data originating at thefurther device and at least partly based on least one speech interactionof the particular party that is discrete from the detected speech data,wherein at least a portion of the adaptation data has been stored on theparticular device associated with the particular party. For example,FIG. 3, e.g., FIG. 3E, shows adaptation data at least partly based ondiscrete speech interaction of particular party separate from detectedspeech data, and has been stored on a particular party-associatedparticular device acquiring from a further device associated with theparticular party module 846 acquiring adaptation data from a furtherdevice associated with the particular party (e.g., a customized gamingcontroller that the user, e.g., the player, brings to use in variousguest video game systems as well as her own), said adaptation dataoriginating at the further device (e.g., the adaptation data is storedon the further device and derived from interactions of the player withthe game system using speech) and at least partly based on at least onespeech interaction of the particular party (e.g., giving voice commandsin a first-person shooter game) that is discrete from the detectedspeech data (e.g., giving voice commands in an online soccer game),wherein at least a portion of the adaptation data has been stored on theparticular device (e.g., a headset used by the player that pullsadaptation data from the particular party, and either passes theadaptation data to the target device, modifies the adaptation data, orperforms some amount of processing on the speech data received throughthe microphone of the headset) associated with the particular party.

Referring again to FIG. 8G, operation 844 may include operation 848depicting acquiring adaptation data from a further device incommunication with the particular device, said adaptation dataoriginating at the further device and at least partly based on least onespeech interaction of the particular party that is discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on the particular device associated with the particularparty. For example, FIG. 3, e.g., FIG. 3E, shows adaptation data atleast partly based on discrete speech interaction of particular partyseparate from detected speech data, and has been stored on a particularparty-associated particular device acquiring from a further device incommunication with the particular device module 348 acquiring adaptationdata from a further device (e.g., a tablet device, e.g., an iPad) incommunication with (e.g., operating on a same network, whether through3G or Wi-Fi communication) the particular device (e.g., a cellulardevice, e.g., an iPhone), said adaptation data originating at thefurther device (e.g., the adaptation data is stored and maintained onthe iPad) and at least partly based on at least one speech interactionof the particular party (e.g., conversations that occurred more than twodays ago) that is discrete from the detected speech data (e.g., speechfrom the user buying a train ticket from an automated train ticketdispensing device), wherein at least a portion of the adaptation datahas been stored on the particular device (e.g., the iPhone receives theadaptation data from the iPad, and determines if any speech interactionshave occurred in the last two days that would result in changing theadaptation data, and, if so, modifies the adaptation data, beforesending the adaptation data to the target device, e.g., the automatedtrain ticket dispensing device) associated with the particular party(e.g., the user).

Referring now to FIG. 8H, operation 844 may include operation 850depicting acquiring adaptation data from a further device that is atleast partially controlled by the particular device, said adaptationdata originating at the further device and at least partly based onleast one speech interaction of the particular party that is discretefrom the detected speech data, wherein at least a portion of theadaptation data has been stored on the particular device associated withthe particular party. For example, FIG. 3, e.g., FIG. 3E, showsadaptation data at least partly based on discrete speech interaction ofparticular party separate from detected speech data, and has been storedon a particular party-associated particular device acquiring from afurther device at least partially controlled by the particular devicemodule 350 acquiring adaptation data from a further device (e.g., alaptop computer plugged into a network) that is at least partiallycontrolled (e.g., has been set up so that portable devices can accessits files and execute limited commands on it) by the particular device(e.g., a tablet device, e.g., an Apple iPad), said adaptation dataoriginating at the further device and at least partly based on at leastone speech interaction of the particular party (e.g., the userprogramming a convection oven) that is discrete from the detected speechdata (e.g., the user programming a microwave oven), wherein at least aportion of the adaptation data (e.g., an utterance ignoring algorithm)has been stored on the particular device (e.g., the Apple iPad)associated with the particular party (e.g., carried by the particularparty).

Referring again to FIG. 8H, operation 840 may include operation 852depicting acquiring adaptation data from a further device, saidadaptation data received by the further device from the particulardevice, and said adaptation data at least partly based on least onespeech interaction of the particular party that is discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on the particular device associated with the particularparty. For example, FIG. 3, e.g., FIG. 3E, shows adaptation data atleast partly based on discrete speech interaction of particular partyseparate from detected speech data, and has been stored on a particularparty-associated particular device acquiring from a further device thatreceived the adaptation data from the particular device module 352acquiring adaptation data (e.g., an uncommon word pronunciation guide),said adaptation data received by the further device (e.g., a portablepersonal navigation system device) from the particular device (e.g., auser's cellular telephone), and said adaptation data at least partlybased on at least one speech interaction of the particular party (e.g.,the user giving commands into his cellular telephone to add contactinformation) that is discrete from the detected speech data (e.g., arequest to lower the windows of the motor vehicle), wherein at least aportion of the adaptation data (e.g., at least one word of the uncommonword pronunciation guide) has been stored on the particular deviceassociated with the particular party (e.g., the user).

Referring now to FIG. 8I, operation 840 may include operation 854depicting acquiring adaptation data, from a further device, saidadaptation data comprising instructions for modifying a pronunciationdictionary, and said adaptation data at least partly based on at leastone speech interaction of the particular party that is discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on the particular device associated with the particularparty. For example, FIG. 3, e.g., FIG. 3F shows adaptation datacomprising instructions for modifying a pronunciation dictionary, saidadaptation data at least partly based on discrete speech interaction ofparticular party separate from detected speech data, and has been storedon a particular party-associated particular device acquiring from afurther device module 354 acquiring adaptation data, from a furtherdevice (e.g., a personal navigation system device), said adaptation datacomprising instructions for modifying a pronunciation dictionary, andsaid adaptation data at least partly based on at least one speechinteraction of the particular party (e.g., requesting directions to thenearest emergency room) that is discrete from the detected speech data(e.g., requesting instructions to the nearest pizza parlor), wherein atleast a portion of the adaptation data has been stored on the particulardevice (e.g., a cellular telephone with GPS positioning enabled)associated with the particular party.

Referring again to FIG. 8I, operation 854 may include operation 856depicting acquiring adaptation data, from a further device, saidadaptation data comprising a first instruction for modifying thepronunciation dictionary based on a first speech interaction of theparticular party and a second instruction for modifying thepronunciation dictionary based on a second speech interaction of theparticular party, and said adaptation data is at least partly based onat least one speech interaction of the particular party that is discretefrom the detected speech data, wherein at least a portion of theadaptation data has been stored on the particular device associated withthe particular party. For example, FIG. 3, e.g., FIG. 3F, showsadaptation data comprising a first instruction for modifying apronunciation dictionary based on a first particular party interactionand a second instruction for modifying a pronunciation dictionary basedon a second particular party interaction, and has been stored on aparticular party-associated particular device acquiring from a furtherdevice module 356 acquiring adaptation data, from a further device(e.g., a tablet device, e.g., a Samsung Galaxy Tab), said adaptationdata comprising a first instruction for modifying the pronunciationdictionary (e.g., “modify a pronunciation of the word ‘twenty’”) basedon a first speech interaction of the particular party (e.g., the userwithdrawing two hundred dollars and requesting twenty dollar bills froman automated teller machine device that accepts speech input) and asecond instruction for modifying the pronunciation dictionary (e.g.,“modify a pronunciation of the word ‘hamburger’”) based on a secondspeech interaction of the particular party (e.g., the user placing alunch order for a hamburger and french fries with an automated drivethru window), and said adaptation data is at least partly based on atleast one speech interaction of the particular party (e.g., the userwithdrawing two hundred dollars and requesting twenty dollar bills froman automated teller machine device that accepts speech input and/or theuser placing a lunch order for a hamburger and French fries) that isdiscrete from the detected speech data (e.g., giving a speech command toan automated ticket taking device), wherein at least a portion of theadaptation data has been stored on the particular device (e.g., acellular telephone device that originally transmitted the adaptationdata to the tablet) associated with the particular party (e.g., owned bythe user).

Referring again to FIG. 8I, operation 856 may include operation 858depicting acquiring adaptation data, from a further device, saidadaptation data comprising a first instruction for modifying thepronunciation dictionary based on a first speech interaction of theparticular party and a second instruction for modifying thepronunciation dictionary based on a second speech interaction of theparticular party, and said adaptation data is at least partly based onat least one speech interaction of the particular party that is discretefrom the detected speech data, wherein the first instruction formodifying the pronunciation data has been stored on the particulardevice associated with the particular party. For example, FIG. 3, e.g.,FIG. 3F, shows adaptation data comprising a first instruction formodifying a pronunciation dictionary based on a first particular partyinteraction and a second instruction for modifying a pronunciationdictionary based on a second particular party interaction, said firstinstruction has been stored on a particular party-associated particulardevice acquiring from a further device module 358 acquiring adaptationdata, from a further device (e.g., a tablet device, e.g., a SamsungGalaxy Tab), said adaptation data comprising a first instruction formodifying the pronunciation dictionary (e.g., “modify a pronunciation ofthe word ‘twenty’”) based on a first speech interaction of theparticular party (e.g., the user withdrawing two hundred dollars andrequesting twenty dollar bills from an automated teller machine devicethat accepts speech input) and a second instruction for modifying thepronunciation dictionary (e.g., “modify a pronunciation of the word‘hamburger’”) based on a second speech interaction of the particularparty (e.g., the user placing a lunch order for a hamburger and frenchfries with an automated drive thru window), and said adaptation data isat least partly based on at least one speech interaction of theparticular party (e.g., the user withdrawing two hundred dollars andrequesting twenty dollar bills from an automated teller machine devicethat accepts speech input and/or the user placing a lunch order for ahamburger and French fries) that is discrete from the detected speechdata (e.g., giving a speech command to an automated ticket takingdevice), wherein the first instruction for modifying the pronunciationdata has been stored on the particular device (e.g., a cellulartelephone device that originally transmitted at least that portion ofthe adaptation data to the tablet) associated with the particular party(e.g., associated to the user with a service contract through acommunication network provider).

Referring now to FIG. 8J, operation 604 may include operation 860depicting generating adaptation data that is at least partly based on atleast one speech interaction of the particular party that is discretefrom the detected speech data, wherein at least a portion of theadaptation data has been stored on the particular device associated withthe particular party. For example, FIG. 3, e.g., FIG. 3G, showsadaptation data at least partly based on discrete speech interaction ofparticular party separate from detected speech data, and has been storedon a particular party-associated particular device generating module 360generating (e.g., creating, modifying, adapting, calculating,developing, evolving, or constructing) adaptation data (e.g., a latentdialogue act matrix)

Referring again to FIG. 8J, operation 604 may include operation 862depicting retrieving adaptation data that is at least partly based on atleast one speech interaction of the particular party that is discretefrom the detected speech data, wherein at least a portion of theadaptation data has been stored on the particular device associated withthe particular party. For example, FIG. 3, e.g., FIG. 3G, showsadaptation data at least partly based on discrete speech interaction ofparticular party separate from detected speech data, and has been storedon a particular party-associated particular device retrieving module 362retrieving (e.g., requesting and receiving, obtaining, gathering,getting, fetching, and/or procuring) adaptation data (e.g., speechdisfluency detection algorithm) that is at least partly based on atleast one speech interaction (e.g., dictating a memorandum using Dragonspeech software with a headset) of the particular party that is discretefrom the detected speech data (e.g., ordering an ice cream cone withchocolate sprinkles from an automated ice cream dispenser), wherein atleast a portion of the adaptation data has been stored on the particulardevice (e.g., a modified USB key that stores adaptation data, that wasplugged into the computer when the memorandum was dictated, therebyretrieving the data) and, at the time of the speech interaction with theautomated ice cream dispenser, is communicating with the automated icecream dispenser, either by being directly plugged into the automated icecream dispenser, or by being plugged into a tablet device carried by theuser, where the tablet device retrieves the adaptation data andtransmits it to the automated ice cream dispenser).

Referring again to FIG. 8J, operation 604 may include operation 864depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party with a particulartype of device, said at least one speech interaction discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on the particular device associated with the particularparty. For example, FIG. 3, e.g., FIG. 3G, shows adaptation data atleast partly based on discrete speech interaction of particular partywith particular type of device separate from detected speech data, andhas been stored on a particular party-associated particular deviceacquiring module 364 acquiring (e.g., retrieving from memory) adaptationdata (e.g., a word and/or syllable dependency parser) that is at leastpartly based on at least one speech interaction of the particular partywith a particular type of device (e.g., a Sony-branded homeentertainment product, e.g., a television, Blu-Ray player, home theatersystem, etc.), said at least one speech interaction discrete from thedetected speech data (e.g., an interaction with a brand newSony-manufactured television), wherein at least a portion of theadaptation data (e.g., the word and/or syllable dependency parser) hasbeen stored on the particular device (e.g., a cellular telephone devicewith an app designed by Sony configured to filter adaptation data)associated with the particular party (e.g., owned by the particularparty).

Referring again to FIG. 8J, operation 864 may include operation 866depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party with the particulartype of device that is a same type of device as a target deviceconfigured to receive the speech data, said at least one speechinteraction discrete from the detected speech data, wherein at least aportion of the adaptation data has been stored on the particular deviceassociated with the particular party. For example, FIG. 3, e.g., FIG.3G, shows adaptation data at least partly based on discrete speechinteraction of particular party with device of same type as targetdevice configured to receive speech data, said discrete interactionseparate from detected speech data, and has been stored on a particularparty-associated particular device acquiring module 366 acquiringadaptation data (e.g., a syllable pronunciation database) that is atleast partly based on at least one speech interaction of the particularparty (e.g., ordering a particular type and flavor of soda from anautomated drink dispensing machine, e.g., “cherry diet Coke with a twistof vanilla”) with the particular type of device (e.g., automated fooddispensing machines) that is a same type of device as a target device(e.g., an automated ice cream dispenser) configured to receive thespeech data (e.g., the particular party ordering a “double scoop ofvanilla with nuts, chocolate sprinkles, and chocolate syrup”), said atleast one speech interaction discrete from the detected speech data,wherein at least a portion of the adaptation data (e.g., the syllablepronunciation database) has been stored on the particular device (e.g.,a “food preference smartcard” that can store, receive, and transmitdata, and that a child can carry with him or her, and that also may beconfigured to prevent the child from ordering food that he or she isallergic to) associated with the particular party (e.g., carried by theuser, e.g., the particular party).

Referring again to FIG. 8J, operation 864 may include operation 868depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party with a device thathas at least one characteristic in common with a target device that isconfigured to receive the speech data, said at least one speechinteraction is discrete from the detected speech data, wherein at leasta portion of the adaptation data has been stored on the particulardevice associated with the particular party. For example, FIG. 3, e.g.,FIG. 3G, shows adaptation data at least partly based on discrete speechinteraction of particular party with device having particularcharacteristic separate from detected speech data, and has been storedon a particular party-associated particular device acquiring module 368acquiring adaptation data (e.g., a syllable pronunciation database) thatis at least partly based on at least one speech interaction of theparticular party (e.g., inputting a playlist via speech) with a device(e.g., a media player) that has at least one characteristic in common(e.g., an ability to play music files) with a target device that isconfigured to receive the speech data (e.g., a speech-enabled clockradio that plays music files), said at least one speech interaction isdiscrete from the detected speech data, wherein at least a portion ofthe adaptation data (e.g., the syllable pronunciation database) has beenstored on the particular device (e.g., the user's cellular telephonedevice) associated with the particular party.

Referring now to FIG. 8K, operation 868 depicting operation 870depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party with a device thatcommunicates on a same type of communication network as the targetdevice that is configured to receive the speech data, said at least onespeech interaction is discrete from the detected speech data, wherein atleast a portion of the adaptation data has been stored on the particulardevice associated with the particular party. For example, FIG. 3, e.g.,FIG. 3G, shows adaptation data at least partly based on discrete speechinteraction of particular party with device communicating on a samecommunication network as target device and separate from detected speechdata, and has been stored on a particular party-associated particulardevice acquiring module 370 acquiring adaptation data (e.g., acontext-based repaired utterance processing matrix) that is at leastpartly based on at least one speech interaction of the particular party(e.g., a speech interaction with the user commanding an officephotocopier) with a device (e.g., the office photocopier) thatcommunicates on a same type of communication network (e.g., local areanetwork, as opposed to 4G LTE, or Bluetooth) as the target device thatis configured to receive the speech data (e.g., an office computer),said at least one speech interaction is discrete from the detectedspeech data (e.g., dictating a memorandum to the office computer),wherein at least a portion of the adaptation data has been stored on theparticular device (e.g., an office-issued device that can transmit,store, and receive adaptation data, e.g., an advanced keycard)associated with the particular party.

Referring again to FIG. 8K, operation 868 may include operation 872depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party with a device thatis configured to carry out a similar function as the target device thatis configured to receive the speech data, said at least one speechinteraction is discrete from the detected speech data, wherein at leasta portion of the adaptation data has been stored on the particulardevice associated with the particular party. For example, FIG. 3, e.g.,FIG. 3G, shows adaptation data at least partly based on discrete speechinteraction of particular party with device configured to carry out asame function as the target device and separate from detected speechdata, and has been stored on a particular party-associated particulardevice acquiring module 372 acquiring adaptation data (e.g., a regionaldialect application algorithm) that is at least partly based on at leastone speech interaction of the particular party with a device (e.g., aportable navigation system) that is configured to carry out a similarfunction as the target device (e.g., an onboard navigation system in amotor vehicle) that is configured to receive the speech data (e.g.,requesting directions on how to get home from the present location),said at least one speech interaction is discrete from the detectedspeech data (e.g., because the interactions are with two similar, butdifferent devices), wherein at least a portion of the adaptation datahas been stored on the particular device (e.g., a cellular telephonedevice).

Referring now to FIG. 8L, operation 868 may include operation 874depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party with a type ofdevice that accepts a same type of input as the target device that isconfigured to receive the speech data, said at least one speechinteraction is discrete from the detected speech data, wherein at leasta portion of the adaptation data has been stored on the particulardevice associated with the particular party. For example, FIG. 3, e.g.,FIG. 3H, shows adaptation data at least partly based on discrete speechinteraction of particular party with device configured to accept a sametype of input as the target device and separate from detected speechdata, and has been stored on a particular party-associated particulardevice acquiring module 374 acquiring adaptation data that is at leastpartly based on at least one speech interaction of the particular party(e.g., ordering food at an automated drive-thru window) with a type ofdevice (e.g., an automated ordering window) that accepts a same type ofinput (e.g., food orders) as the target device (e.g., an automatedterminal inside a restaurant that gives out more detail about a menuoption in response to a speech prompt) that is configured to receive thespeech data (e.g., a request to know more about the Kobe beef entrée),said at least one speech interaction is discrete from the detectedspeech data, wherein at least a portion of the adaptation data has beenstored on the particular device (e.g., a user's tablet device)associated with the particular party (e.g., owned by the user).

Referring now to FIG. 8M, operation 604 may include operation 876depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party with the particulardevice, said at least one speech interaction is discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on the particular device associated with the particularparty. For example, FIG. 3, e.g., FIG. 3H, shows adaptation data atleast partly based on discrete speech interaction of particular partywith particular device separate from detected speech data, and has beenstored on a particular party-associated particular device acquiringmodule 376 acquiring adaptation data (e.g., a list of the way that theparticular party pronounces ten words) that is at least partly based onat least one speech interaction of the particular party (e.g., the usergiving commands to play a particular game to a headset that also cantransmit and receive adaptation data to and from a video game system)with the particular device (e.g., the headset), said at least one speechinteraction is discrete from the detected speech data (e.g., giving anautomated command to the video game system in a first person shooter,e.g., “arm the machine gun”), wherein at least a portion of theadaptation data has been stored on the particular device (e.g., theheadset) associated with the particular party (e.g., has been set up foruse with the user).

Referring again to FIG. 8M, operation 876 may include operation 878depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party with a cellulartelephone device, said at least one speech interaction is discrete fromthe detected speech data, wherein at least a portion of the adaptationdata has been stored on the cellular telephone device associated withthe particular party. For example, FIG. 3, e.g., FIG. 3H, showsadaptation data at least partly based on discrete speech interaction ofparticular party with cellular telephone device separate from detectedspeech data, and has been stored on a particular party-associatedcellular telephone device acquiring module 378 acquiring adaptation data(e.g., instructions for replacing a word frequency table with a modifiedword frequency table that reflects the particular party's word usage)that is at least partly based on at least one speech interaction of theparticular party (e.g., the user) with a cellular telephone device(e.g., playing a word-fill-in based game using speech, which game isdesigned to also generate training data), said at least one speechinteraction is discrete from the detected speech data (e.g., interactingwith an automated drive-thru window), wherein at least a portion of theadaptation data has been stored on the cellular telephone deviceassociated with the particular party.

Referring again to FIG. 8M, operation 878 may include operation 880depicting acquiring adaptation data that is at least partly based on atleast one telephone conversation carried out using the cellulartelephone device, said at least one telephone conversation is differentthan speech that is part of the detected speech data, wherein at least aportion of the adaptation data has been stored on the cellular telephonedevice associated with the particular party. For example, FIG. 3, e.g.,FIG. 3H, shows adaptation data at least partly based on particular partytelephone conversation carried out using cellular telephone deviceseparate from detected speech data, and has been stored on a particularparty-associated cellular telephone acquiring module 380 acquiringadaptation data (e.g., a phrase completion algorithm) that is at leastpartly based on at least one telephone conversation carried out usingthe cellular telephone device, said at least one telephone conversationis different than speech that is part of the detected speech data (e.g.,dictating a memorandum to a speech-enabled computer that also isconfigured to communicate with the cellular telephone device), whereinat least a portion of the adaptation data has been stored on thecellular telephone device associated with the particular party (e.g.,the particular party has a service contract with a communication networkprovider that sold the cellular telephone device to the user at adiscount based on the service contract).

Referring again to FIG. 8M, operation 880 may include operation 882depicting acquiring adaptation data that is at least partly based on atleast one speech instruction given to the cellular telephone device bythe particular party, said at least one speech instruction differentfrom the detected speech data, wherein at least a portion of theadaptation data has been stored on the particular device associated withthe particular party. For example, FIG. 3, e.g., FIG. 3H, showsadaptation data at least partly based on particular party speech commandgiven to cellular telephone device separate from detected speech data,and has been stored on a particular party-associated cellular telephoneacquiring module 382 acquiring adaptation data (e.g., a basicpronunciation adjustment algorithm) that is at least partly based on atleast one speech instruction given to the cellular telephone device bythe particular party (e.g., dictating a text message to be sent to Jennyand Rob), said at least one speech instruction different from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on the particular device associated with the particularparty.

Referring now to FIG. 8N, operation 604 may include operation 884depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party that used one ormore same utterances as speech used in the detected speech data, saidone or more same utterances spoken to a different device than a targetdevice to which the detected speech data is directed. For example, FIG.3, e.g., FIG. 3I shows adaptation data at least partly based on discretespeech interaction of particular party separate from detected speechdata and using same utterance as speech that is part of speech data, andhas been stored on a particular party-associated particular deviceacquiring module 384 acquiring adaptation data (e.g., an emotion-basedpronunciation adjustment algorithm) that is at least partly based on atleast one speech interaction of the particular party (e.g., using voicecommands to operate a motor vehicle control system) that used one ormore same utterances (e.g., spoke one or more of the same words, e.g.,“music,” “play,” “MP3,” and “CD Number Four”) spoken to a differentdevice (e.g., the motor vehicle control system) than a target device towhich the detected speech data is directed (e.g., a home media player).

Referring again to FIG. 8N, operation 604 may include operation 886depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party that used one ormore same utterances, said one or more same utterances spoken at adifferent time than speech used in the detected speech data. Forexample, FIG. 3, e.g., FIG. 3I, shows adaptation data at least partlybased on discrete speech interaction of particular party and using sameutterance as speech that is part of speech data at a different time thanspeech that is part of the speech data acquiring module 386 acquiringadaptation data (e.g., a sentence diagramming path selection algorithm)that is at least partly based on at least one speech interaction of theparticular party (e.g., a player of a speech-controlled video gamesystem playing a soccer game) that used one or more same utterances(e.g., “kick,” “run,” jump,” “control player two”), said one or moresame utterances spoken at a different time (e.g., while playing adifferent game) than speech used in the detected speech data (e.g., theplayer playing a new soccer game at a different time).

Referring again to FIG. 8N, operation 604 may include operation 888depicting acquiring a phoneme database based on one or morepronunciations by the particular party that are discrete from thedetected speech data, wherein at least one entry of the phoneme databasehas been stored on a particular device associated with the particularparty. For example, FIG. 3, e.g., FIG. 3I, shows adaptation datacomprising a phoneme dictionary based on one or more particular partypronunciations, such that at least one entry has been stored on aparticular party-associated particular device acquiring module 388acquiring a phoneme database based on one or more pronunciations by theparticular party (e.g., pronunciations given while a driver is givingcommands to a motor vehicle control system to raise the volume on thestereo, open the sunroof, lower the windows, brighten the interiorlights, and stop using the overdrive mode, because the driver is goingto start driving fast while listening to loud music) that are discretefrom the detected speech data (e.g., the driver, having wrecked hisvehicle, now is using the onboard automated help system to call for helpand describe his situation), wherein at least one entry of the phonemedatabase has been stored on a particular device (e.g., a smart key thatis used to activate the car and store the phoneme database for thatparticular driver, so that a different driver would use a different keyand the vehicle would have a different phoneme database for thedifferent driver) associated with the particular party (e.g., it storesadaptation data that is based at least in part on speech from thedriver).

Referring again to FIG. 8N, operation 604 may include operation 890depicting acquiring a sentence diagramming path selection algorithmbased on at least one speech interaction of the particular party that isdiscrete from the detected speech data, wherein at least a portion ofthe adaptation data has been stored on a particular device associatedwith the particular party. For example, FIG. 3, e.g., FIG. 3I, showsadaptation data comprising a sentence diagramming path selectionalgorithm based on one or more particular party pronunciations, and hasbeen stored on a particular party-associated particular device acquiringmodule 390 acquiring a sentence diagramming path selection algorithmbased on at least one speech interaction of the particular party (e.g.,programming, using speech commands, favorite channels on an oldtelevision made by a particular manufacturer, e.g., Samsung) that isdiscrete from the detected speech data (e.g., programming, using speechcommands, favorite channels on a new flat screen plasma television madeby a different manufacturer, e.g., Panasonic), wherein at least aportion of the adaptation data has been stored on a particular device(e.g., a universal remote control, e.g., manufactured by a stilldifferent manufacturer from either the old television or the newtelevision, e.g., Logitech) associated with the particular party (e.g.,the owner of the universal remote control).

Referring again to FIG. 8N, operation 604 may include operation 892depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party that is discretefrom the detected speech data, wherein at least a portion of theadaptation data was collected by the particular device associated withthe particular party. For example, FIG. 3, e.g., FIG. 3I, showsadaptation data at least partly based on discrete speech interaction ofparticular party separate from detected speech data, and at least partlycollected by a particular party-associated particular device acquiringmodule 392 acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party (e.g., speechinteractions with speech-controlled kitchen devices) that is discretefrom the detected speech data (e.g., controlling a speech-commandedclock radio in the bedroom), wherein at least a portion of theadaptation data was collected by the particular device (e.g., a desktopcomputer that is networked to each of the speech-controlled kitchendevices and the speech-controlled clock radio) associated with theparticular party (e.g., the user has a login on the desktop computer).

Referring again to FIG. 8N, operation 604 may include operation 894depicting acquiring one or more instructions for modifying one or moreportions of a speech recognition component of a target device, saidinstructions at least partly based on at least one speech interaction ofthe particular party that is discrete from the detected speech data,wherein at least a portion of the adaptation data has been stored on aparticular device associated with the particular party. For example,FIG. 3, e.g., FIG. 3I, shows adaptation data comprising instructions formodifying one or more portions of a speech recognition component of atarget device that are at least partly based on one or more particularparty speech interactions, and has been stored on a particularparty-associated particular device acquiring module 394 acquiring one ormore instructions (e.g., modifying one or more parameters of one or morealgorithms) for modifying one or more portions of a speech recognitioncomponent (e.g., a set of logic gates configured to execute one or moreof the algorithms for processing speech) of a target device (e.g., anautomated teller machine device), said instructions at least partlybased on at least one speech interaction of the particular party that isdiscrete from the detected speech data (e.g., based on previous speechinteractions with automated teller machine devices), wherein at least aportion of the adaptation data has been stored on a particular deviceassociated with the particular party (e.g., a cellular telephone deviceowned by the user).

Referring now to FIG. 8P (there is no FIG. 8O to avoid confusing thefigure with a nonexistent Figure “eighty,” e.g., “80”), operation 604may include operation 896 depicting acquiring a location of one or moreinstructions for modifying one or more portions of a speech recognitioncomponent of a target device, said instructions at least partly based onat least one speech interaction of the particular party that is discretefrom the detected speech data, wherein at least a portion of theadaptation data has been stored on a particular device associated withthe particular party. For example, FIG. 3, e.g., FIG. 3J, showsadaptation data comprising a location of instructions for modifying oneor more portions of a speech recognition component of a target devicethat are at least partly based on one or more particular party speechinteractions, and has been stored on a particular party-associatedparticular device acquiring module 396 acquiring a location (e.g., alocation in memory, or a location of a server) of one or moreinstructions for modifying one or more portions of a speech recognitioncomponent (e.g., an order in which speech algorithms are applied) of atarget device (e.g., a computer with speech recognition software andword processing software loaded onto it), said instructions at leastpartly based on at least one speech interaction of the particular partythat is discrete from the detected speech data (e.g., based on at leastone previous dictation of one or more documents), wherein at least aportion of the adaptation data (e.g., the location of one or moreinstructions for modifying one or more portions of a speech recognitioncomponent of a target device) has been stored on a particular device(e.g., a headset worn by the user) associated with the particular party(e.g., set up and associated with the user).

Referring again to FIG. 8P, operation 604 may include operation 898depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party that is discretefrom the detected speech data, wherein at least a portion of theadaptation data is transmitted from the particular device associatedwith the particular party. For example, FIG. 3, e.g., FIG. 3J, showsadaptation data at least partly based on discrete speech interaction ofparticular party separate from detected speech data, and transmittedfrom a particular party-associated particular device acquiring module398 acquiring adaptation data (e.g., an ungrammatical utterance deletionalgorithm) that is at least partly based on at least one speechinteraction of the particular party (e.g., a history of the user'smusical selections for automated, speech-controlled jukeboxes) that isdiscrete from the detected speech data (e.g., selecting a new song atthe speech-commanded jukebox), wherein at least a portion of theadaptation data is transmitted from the particular device (e.g., anear-field communications device held by the user that stores adaptationdata) associated with the particular party).

Referring again to FIG. 8P, operation 604 may include operation 801depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party that is discretefrom the detected speech data, wherein at least a portion of theadaptation data is stored on the particular device associated with theparticular party. For example, FIG. 3, e.g., FIG. 3J, shows adaptationdata at least partly based on discrete speech interaction of particularparty separate from detected speech data, and stored on a particularparty-associated particular device acquiring module 301 acquiringadaptation data (e.g., a set of proper noun pronunciations, e.g., citynames) that is at least partly based on at least one speech interactionof the particular party (e.g., the particular party dictating directionsinto a word processor), wherein at least a portion of the adaptationdata is stored on the particular device (e.g., a USB stick, e.g., thefirst personal device 20A) associated with the particular party (e.g.,the user).

Referring again to FIG. 8P, operation 604 may include operation 803depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party that is discretefrom the detected speech data, wherein at least a portion of theadaptation data is temporarily stored on the particular deviceassociated with the particular party until it is deposited at a remoteserver. For example, FIG. 3, e.g., FIG. 3J, shows adaptation data atleast partly based on discrete speech interaction of particular partyseparate from detected speech data, and is temporarily stored on theparticular-party associated particular device until remote serverdeposit acquiring module 303 acquiring (e.g., receiving from a remoteserver, e.g., Amazon cloud services) adaptation data (e.g., a set ofproper noun pronunciations, e.g., city names) that is at least partlybased on at least one speech interaction of the particular party (e.g.,previous interactions with automated ticket dispensing devices usingspeech) that is discrete from the detected speech data (e.g., speechdata that comes from a speech interaction with an automated train ticketdispensing device located at Union Station in Washington, D.C.), whereinat least a portion of the adaptation data is temporarily stored on theparticular device (e.g., in one or more of the previous interactionswith automated ticket dispensing devices, the particular party'spronunciation of a city is stored on the cellular telephone deviceassociated with the particular party) until it is deposited at a remoteserver (e.g., the Amazon cloud services from where it was retrievedalong with the rest of the adaptation data).

Referring again to FIG. 8P, operation 604 may include operation 805depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party that is discretefrom the detected speech data, wherein at least a portion of theadaptation data was transmitted from a first device to a second deviceusing the particular device associated with the particular party as aconduit configured to facilitate the transmission. For example, FIG. 3,e.g., FIG. 3J, shows adaptation data at least partly based on discretespeech interaction of particular party separate from detected speechdata, and was transmitted from a first device to a second device usingthe particular party-associated particular device as a channelconfigured to facilitate the transaction acquiring module 305 acquiringadaptation data (e.g., a partial pattern tree model) that is at leastpartly based on at least one speech interaction of the particular party(e.g., the user giving speech commands to request a re-route to a GPSnavigation device) that is discrete from the detected speech data (e.g.,the user giving a command to the GPS navigation device to find a cheeseshop), wherein at least a portion of the adaptation data was transmittedfrom a first device (e.g., a GPS navigation device, e.g., GPS navigationdevice 41, that may be good at re-routing traffic but has no informationon cheese shops) to a second device (e.g., an onboard motor vehiclecontrol system, e.g., motor vehicle control system 42, which may be badat re-routing traffic but has an extensive cheese shop database) usingthe particular device (e.g., a smart key device, e.g., smart key 26, ora cellular telephone device) associated with the particular party as aconduit (e.g., the smart key device 26 communicates with the GPSnavigation device 41 and the motor vehicle control system 42) configuredto facilitate (e.g., take one or more steps that aid or assist in) thetransmission of the adaptation data.

Referring now to FIG. 8Q, operation 604 may include operation 807depicting acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party that is discretefrom the detected speech data, wherein at least a portion of theadaptation data originated at the particular device associated with theparticular party. For example, FIG. 3, e.g., FIG. 3K, shows adaptationdata at least partly based on discrete speech interaction of particularparty separate from detected speech data, and at least a portion ofwhich originated at a particular party-associated particular deviceacquiring module 307 acquiring adaptation data (e.g., a discourse markerdetecting module) that is at least partly based on at least one speechinteraction of the particular party that is discrete from the detectedspeech data, wherein at least a portion of the adaptation dataoriginated at the particular device (e.g., a universal remote control,e.g., personal device 22A).

Referring again to FIG. 8Q, operation 604 may include operation 809depicting acquiring adaptation data from a remote location, saidadaptation data at least partly based on at least one speech interactionof the particular party that is discrete from the detected speech data,wherein at least a portion of the adaptation data was transmitted to theremote location from the particular device associated with theparticular party. For example, FIG. 3, e.g., FIG. 3K, shows adaptationdata at least partly based on discrete speech interaction of particularparty separate from detected speech data, and at least a portion ofwhich was transmitted to a remote location from a particularparty-associated particular device receiving from remote location module309 acquiring adaptation data (e.g., an accent-based pronunciationmodification algorithm) from a remote location (e.g., a remote server,e.g., server 110), said adaptation data at least partly based on atleast one speech interaction of the particular party that is discretefrom the detected speech data (e.g., previous commands given to aheadset during an augmented reality gaming session where the headset isworn outside), wherein at least a portion of the adaptation data wastransmitted to the remote location (e.g., the adaptation data collectedfrom the speech interactions with the headset does not stay on theheadset, but is transmitted to a remote location) from the particulardevice (e.g., an augmented reality headset) associated with theparticular party (e.g., being worn by the user).

Referring again to FIG. 8Q, operation 604 may include operation 811depicting receiving adaptation data that is at least partly based on atleast one speech interaction of the particular party that is discretefrom the detected speech data. For example, FIG. 3, e.g., FIG. 3K, showsadaptation data at least partly based on discrete speech interaction ofparticular party separate from detected speech data receiving module 311receiving adaptation data (e.g., a list of the way that the particularparty pronounces ten words) that is at least partly based on at leastone speech interaction of the particular party that is discrete from thedetected speech data (e.g., ordering a triple bacon cheeseburger fromthe automated drive-thru window).

Referring again to FIG. 8Q, operation 604 may include operation 813depicting adding further data to the received adaptation data. Forexample, FIG. 3, e.g., FIG. 3K, shows further data adding to adaptationdata module 313 adding further data (e.g., adding one or more additionalwords to the list of the way that the particular party pronounces tenwords, e.g., the word “bacon,”).

Referring again to FIG. 8Q, operation 813 may include operation 815depicting adding additional adaptation data to the received adaptationdata. For example, FIG. 3, e.g., FIG. 3K, shows additional adaptationdata adding to adaptation data module 315 adding additional adaptationdata (e.g., another algorithm, e.g., adding an accent-basedpronunciation modification algorithm to be executed serially with orparallel to the existing acquired adaptation data) to the receivedadaptation data (e.g., a phrase completion algorithm).

Referring again to FIG. 8Q, operation 813 may include operation 817depicting adding header data identifying an entity that received theadaptation data. For example, FIG. 3, e.g., FIG. 3K, shows header dataidentifying receiving entity adding to adaptation data module 317 addingheader data identifying an entity (e.g., either specific identification,like a MAC address or IP address, specific type identification, such as“I am a cellular telephone device,” e.g., personal device 22B, orgeneral identity information, e.g., “I am not the ultimate destinationof this adaptation data” that received this information) that receivedthe adaptation data (e.g., an emotion-based pronunciation adjustmentalgorithm).

Referring again to FIG. 8Q, operation 813 may include operation 819depicting adding header data identifying an entity that transmitted theadaptation data. For example, FIG. 3, e.g., FIG. 3K, shows header dataidentifying transmitting entity adding to adaptation data module 319adding header data identifying an entity (e.g., specific or general,similarly to as described above, e.g., “received from a universal remotecontrol,” or, e.g., personal device 22A) that transmitted the adaptationdata (e.g., a partial pattern tree model).

FIGS. 9A-9D depict various implementations of operation 606, accordingto embodiments. Referring now to FIG. 9A, operation 606 may includeoperation 902 depicting receiving data regarding a target deviceconfigured to process the speech data. For example, FIG. 4, e.g., FIG.4A, shows data regarding target device configured to process speech datamodule 402 receiving data (e.g., receiving a network name of) regardinga target device (e.g., the network computer “NA80001” that acceptsspeech input and resides on an accounting firm office network)configured to process the speech data (e.g., the network computer isrunning a word processing application configured to receive dictation ofa memorandum).

Referring again to FIG. 9A, operation 902 may include operation 904depicting receiving data comprising an address of a target deviceconfigured to process the speech data. For example, FIG. 4, e.g., FIG.4A, shows data comprising a target device configured to process speechdata address receiving module 404 receiving data comprising an address(e.g., a physical address, either relative or absolute, or a networklocation, e.g., a computer name, or an IP address, or a MAC identifieraddress, or any other piece of information that can be used to derive oridentify where a target device is) of a target device (e.g., anautomated teller machine device) configured to process the speech data(e.g., a request to withdraw two hundred dollars from a savingsaccount).

Referring again to FIG. 9A, operation 902 may include operation 906depicting receiving data comprising a location of a target deviceconfigured to process the speech data. For example, FIG. 4, e.g., FIG.4A, shows data comprising a target device configured to process speechdata location receiving module 406 receiving data comprising a location(e.g., a particular piece of architecture internal to a device, e.g.,processor 152) of a target device (e.g., a particular portion of thetarget device, e.g., the computer, that is configured to process thespeech, e.g., a portion of a chip whose logic gates have been configuredto process the speech data) configured to process the speech data (e.g.,dictation of a letter to the editor regarding a political topic).

Referring again to FIG. 9A, operation 606 may include operation 908depicting determining a location of a target device that is adestination of one or more of the adaptation data and the speech data.For example, FIG. 4, e.g., FIG. 4A, shows target device location asdestination of one or more of the adaptation data and the speech datadetermining module 408 determining a location (e.g., a location on anetwork, a physical location, a relative location with respect to one ormore of the particular party and the particular device, a virtuallocation, a location on a network map, a location within a computerarchitecture, a location within a software architecture, and the like)of a target device (e.g., a video game system) that is a destination ofone or more of the adaptation data (e.g., a sentence diagramming pathselection algorithm) and the speech data (e.g., giving a speech commandwithin a first person shooter to lob a grenade).

Referring again to FIG. 9A, operation 908 may include operation 970depicting determining a location on a network of a target device that isa destination of one or more of the adaptation data and the speech data.For example, FIG. 4, e.g., FIG. 4A, shows target device network locationas destination of one or more of the adaptation data and the speech datadetermining module 470 determining a location on a network (e.g., alocation, either a computer name, a login name, a MAC, IP, or otheraddress, or similar, of a group of one or more computers and associateddevices that are connected by communications facilities).

Referring again to FIG. 9A, operation 606 may include operation 972depicting obtaining a device name of a destination of one or more of theadaptation data and the speech data. For example, FIG. 4, e.g., FIG. 4A,shows device name of destination of one or more of the adaptation dataand the speech data obtaining module 472 obtaining a device name (e.g.,one or more of a network identification name, a computer name, acomputer description, an internal identifier, a numeric sequence (e.g.,a MAC or IP address) of a destination of one or more of the adaptationdata (e.g., a stochastic state transition network) and the speech data(e.g., giving a speech command to create 25 copies at 11×17).

Referring again to FIG. 9A, operation 606 may include operation 974depicting obtaining a type of device for which the one or more of theadaptation data and the speech data is a destination. For example, FIG.4, e.g., FIG. 4A, shows type of device for which one or more of theadaptation data and the speech data is a destination obtaining module474 obtaining a type of device (e.g., a category of device (e.g.,televisions, blenders, microwave ovens, tablet PCs), a broader categoryof device (e.g., kitchen appliances, home theater components), a typebased on a type of input they receive (e.g., devices that have a “stop”“fast-forward” and “play” button, devices that have a temperaturecontrol, devices that have a volume), or a particular manufacturer(e.g., “a Kenmore device,” or “a Samsung device”) for which the one ormore of the adaptation data (e.g., instructions for replacing a wordfrequency table with a modified word frequency table that reflects theparticular party's word usage) and the speech data is a destination.

Referring again to FIG. 9A, operation 606 may include operation 910depicting determining a program component that is configured to performprocessing on one or more of the adaptation data and the speech data.For example, FIG. 4, e.g., FIG. 4A, shows program component configuredto perform processing on one or more of the adaptation data and thespeech data determining module 410 determining a program component(e.g., a built-in component of a complex word processor) that isconfigured to perform processing (e.g., take one or more stepsmanipulating the data of) on one or more of the adaptation data (e.g., aregional dialect application algorithm) and the speech data (e.g.,dictation of a letter to the editor of a newspaper).

Referring again to FIG. 9A, operation 606 may include operation 912depicting determining a program component that is a destination of oneor more of the adaptation data and the speech data. For example, FIG. 4,e.g., FIG. 4A, shows software component as destination of one or more ofthe adaptation data and the speech data determining module 412determining a program component (e.g., a program that runs in thebackground of an operating system, receives speech data, and performsprocessing on the speech data) that is a destination of one or more ofthe adaptation data and the speech data (e.g., dictation of a novelbeing written in a user's spare time).

Referring again to FIG. 9A, operation 912 may include operation 914depicting selecting between an operating system component and anapplication component as a destination of one or more of the adaptationdata and the speech data. For example, FIG. 4, e.g., FIG. 4A, showsselection between application component and operating system componentas destination of one or more of the adaptation data and the speech dataselecting module 414 selecting (e.g., choosing, based on a determinationof which component should perform the job) between an operating systemcomponent (e.g., an component built into the operating system, e.g.,Microsoft Windows or Apple iOS, that is configured to perform processingon the speech data) and an application component (e.g., a simple wordprocessor, e.g., Microsoft's Notepad) as a destination of one or more ofthe adaptation data and the speech data (e.g., dictation of a groceryshopping list).

Referring now to FIG. 9B, operation 606 may include operation 916depicting acquiring data regarding one or more other devices configuredto process detected speech data. For example, FIG. 4, e.g., FIG. 4B,shows data regarding at least one other device configured to processdetected speech data obtaining module 416 acquiring data regarding oneor more other devices (e.g., a list of other devices, e.g., GPSnavigation devices, within communication range, and their capability offinding wineries) configured to (e.g., capable of) process detectedspeech data (e.g., converted data of a user placing a fast-food order).

Referring again to FIG. 9B, in some embodiments in which operation 606includes operation 916, operation 606 also may include operation 918depicting determining a destination of the detected speech data based onthe data regarding one or more other devices configured to processdetected speech data. For example, FIG. 4, e.g., FIG. 4B, showsdestination of the detected speech data determining based on acquireddata regarding at least one other device determining module 418determining a destination of the detected speech data (e.g., determiningthat there is a GPS navigation system within communication range thathas a good database of wineries) based on the data regarding one or moredevices (e.g., GPS navigation systems and their ability to findwineries) configured to process detected speech data (“direct me to thenearest winery that has Chateau Mont Blanc Rojo”)

Referring again to FIG. 9B, operation 916 may include operation 920depicting detecting one or more other devices configured to processdetected speech data. For example, FIG., 4, e.g., FIG. 4B, shows atleast one or more other device configured to process detected speechdata detecting module 420 detecting (e.g., receiving broadcasted signalsfrom) one or more other devices (e.g., speech-enabled electronic devicesthat are part of a home theater system) configured to process speechdata (e.g., “raise the volume five units”).

Referring again to FIG. 9B, operation 916 may include operation 922depicting acquiring data regarding a number of other devices configuredto process detected speech data. For example, FIG. 4, e.g., FIG. 4B,shows data regarding a number of the at least one other devicesconfigured to process detected speech data obtaining module 422acquiring data regarding a number of other devices (e.g., three otherdevices) configured to process detected speech data (e.g. “set the DVDrecorder to record channel 259 at 5:30 pm”)

Referring again to FIG. 9B, operation 916 may include operation 924depicting acquiring data regarding one or more other devices configuredto process detected speech data as part of the adaptation data. Forexample, FIG. 4, e.g., FIG. 4B, shows data regarding at least one otherdevice configured to process detected speech data acquiring fromadaptation data module 424 acquiring data regarding one or more otherdevices (e.g., acquiring a list of other devices that was prepared byone or more devices and stored in the adaptation data) configured toprocess detected speech data (e.g., “decrease temperature to 72degrees”) as part of the adaptation data (e.g., a phoneme pronunciationdatabase that has header information including list of one or more otherdevices).

Referring again to FIG. 9B, operation 916 may include operation 926depicting detecting one or more other devices configured to processdetected speech data. For example, FIG. 4, e.g., FIG. 4B, showsdetecting at least one or more other devices configured to processdetected speech data module 426 detecting (e.g., pinging various portson a network to determine whether devices are connected to the network,or using an infrared scanner to determine how many actively processingdevices are within detection range of the scanner) one or more otherdevices (e.g., components of a home theater system) configured toprocess detected speech data (e.g., “shut off the television after onehour”).

Referring again to FIG. 9B, operation 916 may include operation 928depicting determining whether the detected speech data is intended to beapplied by one of the one or more other devices. For example, FIG. 4,e.g., FIG. 4B, shows determining whether detected speech data isintended to be applied by one of the one or more other devices module428 determining whether the detected speech data (e.g., “shut off thetelevision after one hour”) is intended to be applied by one of the oneor more other devices (e.g., determining whether one or more of theother devices is a television, and determining that the detected speechdata is intended to be applied by a television).

Referring again to FIG. 9B, operation 916 may include operation 930depicting detecting one or more other devices configured to processdetected speech data. For example, FIG. 4, e.g., FIG. 4B, showsdetecting one or more other devices configured to process detectedspeech data module 430 detecting (e.g., obtaining one or more pieces ofdata regarding) one or more other devices configured to process detectedspeech data (e.g., “set the personal video recorder to record thetelevision show “Friends”).

Referring again to FIG. 9B, operation 916 may include operation 932depicting transmitting a signal to the one or more other devicesrequesting data regarding a capability of the one or more other devicesconfigured to process detected speech data. For example, FIG. 4, e.g.,FIG. 4B, shows signal requesting data regarding a capability of the oneor more other devices transmitting module 432 transmitting a signal(e.g., communicating a request for data) to the one or more otherdevices (e.g., in a home theater setting, the one or more other devicesmay be the television, the receiver, the cable box, the CD player, theDVD player, the Blu-Ray player, the personal video recorder, the videogame system, the universal remote control, seat controls for the seats,climate control for the room, lighting control for the room, and thepersonal computer which may control portions of the system, store media,or perform other functions) configured to process detected speech data(e.g., any or all of the devices in the home theater setting may bespeech-enabled).

Referring again to FIG. 9B, operation 916 may include operation 934depicting receiving data regarding the capability of the one or moreother devices configured to process detected speech data. For example,FIG. 4, e.g., FIG. 4B, shows data regarding capability of the one ormore other devices receiving module 434 receiving data regarding thecapability (e.g., whether a device can receive speech, whether it canprocess speech, what algorithms it uses to process speech, what arewords in the device's vocabulary that it understands, how does thedevice deal with different speech characteristics, does the device haveprofile information of a person stored on the device, does the devicehave speech profile information of a person stored on the device, doesthe device have a network connection, and the like) of the one or moreother devices (e.g., the devices in the home theater system) configuredto process detected speech data (e.g., “set the personal video recorderto record the television show “Friends”).

Referring now to FIG. 9C, operation 916 may include operation 936depicting detecting one or more other devices configured to processdetected speech data. For example, FIG. 4, e.g., FIG. 4C, shows one ormore other devices configured to process detected speech data detectingmodule 436 detecting (e.g., retrieving information from an enterprisenetwork regarding machines that are configured to process the detectedspeech data, e.g., a secured door, a floating computer, a telephone, acopier machine, and the like) or more other devices (e.g., other deviceson the network, as described above) configured to process speech data(e.g., “make 25 copies of this at 85% contrast”).

Referring again to FIG. 9C, operation 916 may include operation 938depicting receiving data regarding a capability of the detected one ormore other devices configured to process detected speech data. Forexample, FIG. 4, e.g., FIG. 4C, shows capability of the detected one ormore other devices configured to process detected speech data receivingmodule 438 receiving data (e.g., from the devices themselves) regardinga capability (e.g., whether a device can receive speech, whether it canprocess speech, what algorithms it uses to process speech, what arewords in the device's vocabulary that it understands, how does thedevice deal with different speech characteristics, does the device haveprofile information of a person stored on the device, does the devicehave speech profile information of a person stored on the device, doesthe device have a network connection, whether the user has properauthority or clearance to use the device, whether the device iscurrently functioning, what it will cost in terms of available processorand/or storage resources to use the device to process the speech data,which device most evenly uses the resources on the network, which deviceis the newest, which device is the fastest, and the like) of thedetected one or more devices (e.g., enterprise network machines, such asa secured door, a floating computer, a telephone, a copier machine, andthe like) configured to process detected speech data (e.g., “make 25copies of this at 85% contrast”).

Referring again to FIG. 9C, operation 938 may include operation 940depicting receiving data regarding the capability of the one or moreother devices configured to process detected speech data from a furtherdevice that is not one of the one or more other devices. For example,FIG. 4, e.g., FIG. 4C, shows capability of the detected one or moreother devices configured to process detected speech data receiving froma device that is not one of the one or more other devices module 440receiving data regarding the capability (e.g., whether a device canreceive speech, whether it can process speech, what algorithms it usesto process speech, what are words in the device's vocabulary that itunderstands, how does the device deal with different speechcharacteristics, does the device have profile information of a personstored on the device, does the device have speech profile information ofa person stored on the device, does the device have a networkconnection, whether the user has proper authority or clearance to usethe device, whether the device is currently functioning, what it willcost in terms of available processor and/or storage resources to use thedevice to process the speech data, which device most evenly uses theresources on the network, which device is the newest, which device isthe fastest, and the like) of the one or more devices configured toprocess detected speech data (e.g., “make 25 copies of this at 85%contrast”) from a further device (e.g., a network device located on anenterprise network that is specifically configured to manage resourcesfor the network, e.g., processing speech data resources) that is not oneof the one or more devices (e.g., the network device may not acceptspeech data, and may not be configured to process that particularcommand of, e.g., “make 25 copies of this at 85% contrast.”)

Referring again to FIG. 9C, operation 940 may include operation 942depicting receiving data regarding the capability of the one or moreother devices configured to process detected speech data from a furtherdevice that is configured to communicate on a same network as the one ormore other devices. For example, FIG. 4, e.g., FIG. 4C, shows capabilityof the detected one or more other devices configured to process detectedspeech data receiving from a device configured to communicate on a samecommunication network as the one or more other devices module 442receiving data regarding the capability (e.g., whether a device canreceive speech, whether it can process speech, what algorithms it usesto process speech, what are words in the device's vocabulary that itunderstands, how does the device deal with different speechcharacteristics, does the device have profile information of a personstored on the device, does the device have speech profile information ofa person stored on the device, does the device have a networkconnection, whether the user has proper authority or clearance to usethe device, whether the device is currently functioning, what it willcost in terms of available processor and/or storage resources to use thedevice to process the speech data, which device most evenly uses theresources on the network, which device is the newest, which device isthe fastest, and the like) of the one or more other devices configuredto process detected speech data from a further device that is configuredto communicate on a same network (e.g., the internal corporate networkon which the devices communicate, which may be a subset or a subnet of alarger network) as the one or more other devices (e.g., enterprisenetwork machines, such as a secured door, a floating computer, atelephone, a copier machine, and the like).

Referring again to FIG. 9C, operation 940 may include operation 944depicting receiving data regarding the capability of the one or moreother devices configured to process detected speech data from a furtherdevice that is at least partially controlled by a same entity thatcontrols at least one of the one or more other devices. For example,FIG. 4, e.g., FIG. 4C, shows capability of the detected one or moreother devices configured to process detected speech data receiving froma device at least partially controlled by a same entity that controls atleast one of the one or more other devices module 444 receiving dataregarding the capability (e.g., whether a device can receive speech,whether it can process speech, what algorithms it uses to processspeech, what are words in the device's vocabulary that it understands,how does the device deal with different speech characteristics, does thedevice have profile information of a person stored on the device, doesthe device have speech profile information of a person stored on thedevice, does the device have a network connection, and the like) of theone or more other devices (e.g., multiple portions of a home securitysystem, e.g., a door lock as one device, an alarm as another device, asafe as a third device) configured to process speech data (e.g.,“activate the alarm”) from a further device (e.g., an always-on homemanagement system with battery backup that manages the devices andstores data, and in some embodiments, handles some of the processing)that is at least partially controlled by a same entity (e.g., the usercontrols the management device as well as each of the one or more otherdevices) that controls at least one of the one or more other devices.

Referring again to FIG. 9C, operation 940 may include operation 946depicting receiving data regarding the capability of the one or moreother devices configured to process detected speech data from a furtherdevice that is configured to provide one or more services to at leastone of the one or more other devices. For example, FIG. 4, e.g., FIG.4C, shows capability of the detected one or more other devicesconfigured to process detected speech data receiving from a deviceconfigured to provide one or more services to at least one of the one ormore other devices module 446 receiving data regarding the capability ofthe one or more other devices configured to process detected speech data(e.g., giving a speech command to an automated drive-thru window) from afurther device (e.g., a motor vehicle command system) that is configuredto provide one or more services to at least one of the one or more otherdevices (e.g., a GPS navigation system, an media playing system, anemergency services calling system, that all use at least a portion ofthe motor vehicle command system for processing).

Referring now to FIG. 9D, operation 606 may include operation 948depicting acquiring other device data regarding a capability of one ormore other devices configured to process detected speech data. Forexample, FIG. 4, e.g., FIG. 4D, shows other device data regarding acapability of one or more other devices configured to process detectedspeech data obtaining module 448 acquiring other device data regarding acapability of one or more other devices (e.g., information regardingalgorithms, or amount of storage, or processing power, or a combinationthereof) configured to process detected speech data (e.g., a command toturn off).

Referring again to FIG. 9D, operation 606 may include operation 950depicting determining a destination of the one or more of the adaptationdata and the speech data at least partly based on the acquired otherdevice data. For example, FIG. 4, e.g., FIG. 4D, shows destination forone or more of the adaptation data and the speech data determining atleast partly based on the acquired other device data module 450determining a destination of the one or more of the adaptation data(e.g., a phrase completion algorithm) and the speech data (e.g., acommand to turn off) at least partly based on the acquired other devicedata (e.g., data indicating which device is currently on).

Referring again to FIG. 9D, operation 948 may include operation 952depicting acquiring other device data regarding an amount of availablememory of one or more detected other devices. For example, FIG. 4, e.g.,FIG. 4D, shows other device data regarding an amount of available memoryfor one or more detected other devices obtaining module 452 acquiringother device data regarding an amount of available memory of one or moredetected other devices (e.g., a laptop computer, a netbook, a tabletcomputer, a smartphone, and a desktop computer).

Referring again to FIG. 9D, operation 948 may include operation 954depicting acquiring other device data regarding an amount of availableprocessor capacity of one or more detected other devices. For example,FIG. 4, e.g., FIG. 4D, shows other device data regarding an amount ofavailable processor capacity for one or more detected other devicesobtaining module 454 acquiring other device data regarding an amount ofavailable processor capacity of one or more detected other devices(e.g., a laptop computer, a netbook, a tablet computer, a smartphone,and a desktop computer).

Referring again to FIG. 9D, operation 948 may include operation 956depicting acquiring other device data regarding a speech data processingcapability of one or more detected other devices. For example, FIG. 4,e.g., FIG. 4D, shows other device data regarding a speech dataprocessing capability for one or more detected other devices obtainingmodule 456 acquiring other device data regarding a speech dataprocessing capability (e.g., an algorithm, a process, a selection ofalgorithms to choose from, a size of the data pool to draw from, thelogic used to process speech, the hardware used to receive the speech,one or more filters used, or any combination thereof) of one or moredetected other devices (e.g., a GPS navigation system).

Referring again to FIG. 9D, operation 956 may include operation 958depicting acquiring other device data regarding one or more speechprocessing models available to the one or more detected other devices.For example, FIG. 4, e.g., FIG. 4D, shows other device data regardingone or more available speech models for one or more detected otherdevices obtaining module 458 acquiring other device data regarding oneor more speech processing models (e.g., a hidden Markov model) availableto the one or more detected other devices (e.g., a speech-enabledblender, a speech-enabled convection oven, and a speech-enabled juicer).

Referring again to FIG. 9D, operation 956 may include operation 960depicting acquiring other device data regarding one or more speechprocessing algorithms available to the one or more detected otherdevices. For example, FIG. 4, e.g., FIG. 4D, shows other device dataregarding one or more available speech algorithms for one or moredetected other devices obtaining module 460 acquiring other device dataregarding one or more speech processing algorithms (e.g., anaccent-based pronunciation modification algorithm and a sentencediagramming path selection algorithm) available to (e.g., either storedon or retrievable by) the one or more detected other devices (e.g., aspeech-enabled video game system).

Referring again to FIG. 9D, operation 960 may include operation 962depicting acquiring other device data regarding one or more speechprocessing algorithms available to a motor vehicle control system and aportable navigation system. For example, FIG. 4, e.g., FIG. 4D, showsother device data regarding one or more available speech algorithms fora motor vehicle control system and a portable navigation systemobtaining module 462 acquiring other device data regarding one or morespeech processing algorithms (e.g., a non-lexical vocable removalalgorithm and a speech disfluency detection) available to a motorvehicle control system and a portable navigation system.

Referring again to FIG. 9D, operation 962 may include operation 964depicting acquiring other device data including data that the motorvehicle control system has an available hidden Markov model and theportable navigation system has an available constrained maximumlikelihood transformation. For example, FIG. 4, e.g., FIG. 4D, showsother device data regarding an available hidden Markov model for a motorvehicle control system and an available constrained maximum likelihoodtransformation for the portable navigation system obtaining module 464acquiring other device data including data that the motor vehiclecontrol system has an available hidden Markov model and the portablenavigation system has an available constrained maximum likelihoodtransformation.

FIGS. 10A-10D depict various implementations of operation 608, accordingto embodiments. Referring now to FIG. 10A, operation 608 may includeoperation 1002 depicting transmitting one or more of the speech data andthe adaptation data to a target device acquired as the destination. Forexample, FIG. 5, e.g., FIG. 5A, shows acquired destination of one ormore of the adaptation data and the speech data transmitting to targetdevice module 502 transmitting one or more of the speech data (e.g., afood order) and the adaptation data (e.g., a context-based repairedutterance processing matrix) to a target device (e.g., an automateddrive-thru window) acquired as the destination.

Referring again to FIG. 10A, operation 1002 may include operation 1004depicting transmitting one or more of the speech data and the adaptationdata to a target device acquired as the destination, said transmittingoccurring via a communication network. For example, FIG. 5, e.g., FIG.5A, shows target device acquired via a communication network asdestination of one or more of the adaptation data and the speech datatransmitting to target device module 504 transmitting one or more of thespeech data (e.g., “give me directions to the nearest Best Buyelectronics store”) and the adaptation data (e.g., a discourse markerdetecting module) to a target device acquired as the destination (e.g.,a motor vehicle control system, e.g., motor vehicle control system 42),said transmitting occurring via a communication network (e.g., over aninternal Bluetooth network set up inside the vehicle).

Referring again to FIG. 10A, operation 1002 may include operation 1006depicting transmitting the detected speech data to the target deviceacquired as the destination. For example, FIG. 5, e.g., FIG. 5A, showsdetected speech data to target device acquired as destinationtransmitting module 506 transmitting the detected speech data (e.g.,“withdraw two hundred dollars from my savings account”) to the targetdevice (e.g., in a row of speech-enabled automated teller machinedevices, delivering to the third automated teller machine device fromthe left).

Referring again to FIG. 10A, operation 1006 may include operation 1008depicting converting detected speech data into data that is recognizableby the target device. For example, FIG. 5, e.g., FIG. 5A, shows detectedspeech data converting into target device recognizable data module 508converting detected speech data (e.g., speech of “open the web browser”)into data that is recognizable by the target device (e.g., by convertingreceived audio data as speech into byte format for transmission andprocessing).

Referring again to FIG. 10A, operation 1006 may include operation 1010depicting transmitting the converted detected speech data to the targetdevice acquired as the destination. For example, FIG. 5, e.g., FIG. 5A,shows converted detected speech data transmitting to target deviceacquired as destination module 510 transmitting the converted detectedspeech data (e.g., the converted “open the web browser” into byteformat) to the target device (e.g., a speech-enabled tablet computer)acquired as the destination (e.g., as an example, a particular devicehas determined that of the detected devices, only the speech-enabledtablet computer has a web browser).

Referring again to FIG. 10A, operation 1008 may include operation 1012depicting converting detected speech data into data that is recognizableby the target device, wherein said detected speech data isunrecognizable to the target device prior to conversion. For example,FIG. 5, e.g., FIG. 5A, shows detected target device incomprehensiblespeech data converting into target device recognizable data module 512converting detected speech data into data that is recognizable by thetarget device (e.g., by decompressing or decrypting the speech data),wherein said detected speech data is unrecognizable to the target deviceprior to conversion (e.g., because of memory, processor, or because ofsecurity reasons, the target device is unable to decompress or decryptthe speech data).

Referring again to FIG. 10A, operation 1008 may include operation 1014depicting converting detected speech data into data that is recognizableby a target device, wherein the target device is configured to processthe converted data more quickly than the target device is configured toprocess speech data. For example, FIG. 5, e.g., FIG. 5A, shows detectedspeech data converting into data recognizable by a target deviceconfigured to process converted data more quickly than unconverted datamodule 514 converting detected speech data into data that isrecognizable (e.g., the detected speech data is filtered using a noiselevel dependent filtration algorithm) by a target device (e.g., aspeech-enabled media player, e.g., media player device 52), wherein thetarget device is configured to process the converted data (e.g., thespeech data that has been filtered) more quickly than the target deviceis configured to process speech data (e.g., the target device, e.g.,which may process everything sound it picks up, does not have to processsounds that should have been filtered out before determining that thesounds do not correspond to words).

Referring again to FIG. 10A, operation 1014 may include operation 1050depicting converting detected speech data into data that is recognizableby a target device, based on a rule that requires conversion of thedetected speech data. For example, FIG. 5, e.g., FIG. 5A, shows detectedspeech data converting into data recognizable by a target deviceconfigured to process converted data more quickly than unconverted databased on a requiring conversion rule module 550 converting detectedspeech data (e.g., data corresponding to a speech command to change theinput to HDMI-1) into data that is recognizable (e.g., a part-of-speechlabeling algorithm) by a target device (e.g., an audio/visual receiver,e.g., receiver device 51), based on a rule that requires conversion ofthe detected speech data (e.g., that always adds part-of-speech labelingto ease processing by target devices).

Referring again to FIG. 10A, operation 1014 may include operation 1052depicting converting detected speech data into data that is recognizableby a target device, based on feedback received from the target deviceindicating that the target device is configured to process the converteddata more quickly than the target device is configured to process speechdata. For example, FIG. 5, e.g., FIG. 5A, shows detected speech dataconverting into data recognizable by a target device configured toprocess converted data more quickly than unconverted data based ontarget device feedback module 552 converting detected speech data (e.g.,“raise the volume five notches”) into data that is recognizable (e.g.,by converting the speech into a data structure of sentence-diagrammedwords, e.g., using a sentence diagramming path selection algorithm) by atarget device (e.g., a speech-enabled television device, e.g.,television device 53), based on feedback received from the target device(e.g., data indicating that the television can quickly handle the datastructure of sentence-diagrammed words) indicating that the targetdevice is configured to process the converted data (e.g., the datastructure holding the sentence-diagrammed words) more quickly than thetarget device is configured to process speech data (e.g., the raw speechreceived at a microphone from the user 105).

Referring now to FIG. 10B, operation 608 may include operation 1016depicting applying one or more filters to the detected speech data, saidone or more filters specified by the acquired adaptation data. Forexample, FIG. 5, e.g., FIG. 5B, shows one or more filters specified bythe acquired adaptation data applying to detected speech data module 516applying one or more filters (e.g., low grade sound filtration) to thedetected speech data (e.g., a request to withdraw two hundred dollarsfrom a checking account), said one or more filters specified by theacquired adaptation data (e.g., the acquired adaptation data containsthe algorithm, parameters for the algorithm, or both).

Referring again to FIG. 10B, operation 608 may include operation 1018depicting transmitting the detected speech data to which the one or morefilters have been applied to the acquired destination. For example, FIG.5, e.g., FIG. 5B, shows filter-applied detected speech data transmittingto acquired destination module 1018 transmitting the detected speechdata (e.g., the request to withdraw two hundred dollars) to which theone or more filters have been applied to the acquired destination (e.g.,an automated teller machine device that the user is standing directly infront of, e.g., the identity of the device is not known, but rather itsposition relative to the user).

Referring again to FIG. 10B, operation 1016 may include operation 1020depicting applying a filter to remove non-lexical vocables from thedetected speech data, said one or more filters defined by the acquiredadaptation data. For example, FIG. 5, e.g., FIG. 5B, shows non-lexicalvocable removal filter specified by the acquired adaptation dataapplying to detected speech data module 520 applying a filter to removenon-lexical vocables from the detected speech data, said one or morefilters defined by the acquired adaptation data (e.g., the acquiredadaptation data is a list of the particular party's pronunciation ofcommon non-lexical vocables used by the particular party).

Referring now to FIG. 10B, operation 608 may include operation 1022depicting transmitting one or more of the speech data and the adaptationdata to a particular location in memory. For example, FIG. 5, e.g., FIG.5B, shows one or more of speech data and adaptation data transmitting toparticular memory location module 522 transmitting one or more of thespeech data and the adaptation data (e.g., a latent dialogue act matrix)to a particular location in memory (e.g., transmitting to an addressx0000FFDD, or transmitting to removable storage media)

Referring again to FIG. 10B, operation 608 may include operation 1024depicting transmitting one or more of the speech data and the adaptationdata to a particular component of a target device. For example, FIG. 5,e.g., FIG. 5B, shows one or more of speech data and adaptation datatransmitting to particular target device component module 524transmitting one or more of the speech data (e.g., dictation of amemorandum) and the adaptation data (e.g., an ungrammatical utterancedeletion algorithm) to a particular component (e.g., a particular chipon a board, or a particular software module, e.g., a word processingsoftware) of a target device (e.g., a computer).

Referring again to FIG. 10B, operation 1024 may include operation 1026depicting transmitting the adaptation data to a speech recognitioncomponent of the target device. For example, FIG. 5, e.g., FIG. 5B,shows one or more of speech data and adaptation data transmitting totarget device speech recognition component module 526 transmitting theadaptation data (e.g., an utterance ignoring algorithm) to a speechrecognition component of the target device (e.g., a central processor ofan automated teller machine device).

Referring again to FIG. 10B, operation 1026 may include operation 1028depicting transmitting instructions for replacing a word frequency tablewith a modified word frequency table that reflects the particularparty's word usage to a speech recognition component of the targetdevice. For example, FIG. 5, e.g., FIG. 5B, shows adaptation datacomprising instructions for replacing a word frequency table with amodified word frequency table reflecting particular party word usagetransmitting to target device speech recognition component module 528transmitting instructions for replacing a word frequency table with amodified word frequency table that reflects the particular party's wordusage to a speech recognition component of the target device (e.g.,target device 30A, e.g., an automated drive-thru window).

Referring again to FIG. 10B, operation 1028 may include operation 1030depicting transmitting instructions for replacing a word frequency tablewith a modified word frequency table that reflects the particularparty's word usage to a speech recognition component of a motor vehiclecontrol system. For example, FIG. 5, e.g., FIG. 5B, shows adaptationdata comprising instructions for replacing a word frequency table with amodified word frequency table reflecting particular party word usagetransmitting to a motor vehicle control system speech recognitioncomponent module 530 transmitting instructions for replacing a wordfrequency table with a modified word frequency table that reflects theparticular party's word usage to a speech recognition component of amotor vehicle control system.

Referring now to FIG. 10C, operation 608 may include operation 1032depicting transmitting one or more of the speech data and the adaptationdata to a further device, said one or more of the speech data and theadaptation data configured to be processed by a target device. Forexample, FIG. 5, e.g., FIG. 5B, shows one or more of speech data andadaptation data configured to be processed by a target devicetransmitting to further device module 532 transmitting one or more ofthe speech data (e.g., a command to increase the brightness to 100) andthe adaptation data (e.g., a syllable pronunciation database) to afurther device (e.g., a universal remote control, e.g., a personaldevice 22A), said one or more of the speech data (e.g., a command toincrease the brightness to 100) and the adaptation data (e.g., asyllable pronunciation database) configured to be processed by a targetdevice (e.g., an audio/visual receiver, e.g., receiver device 51).

Referring again to FIG. 10C, operation 1032 may include operation 1034depicting transmitting one or more of the speech data and the adaptationdata to a personal navigation device, said one or more of the speechdata and the adaptation data configured to be processed by a motorvehicle control device. For example, FIG. 5, e.g., FIG. 5B, shows one ormore of speech data and adaptation data configured to be processed by amotor vehicle control device transmitting to a personal navigationdevice module 534 transmitting one or more of the speech data (e.g.,“give me directions to the nearest Chumley's All-You-Can-Eat Fried Codrestaurant”) and the adaptation data (e.g., a set of proper nounpronunciations) to a personal navigation device (e.g., GPS navigationdevice 41), said one or more of the speech data (e.g., “give medirections to the nearest Chumley's All-You-Can-Eat Fried Codrestaurant”) and the adaptation data (e.g., a set of proper nounpronunciations) configured to be processed by a motor vehicle controldevice (e.g., motor vehicle control system 42, e.g., which, in someembodiments, includes onboard navigation, and in some embodiments,includes listings of restaurants, including Chumley's).

All of the above U.S. patents, U.S. patent application publications,U.S. patent applications, foreign patents, foreign patent applicationsand non-patent publications referred to in this specification and/orlisted in any Application Data Sheet, are incorporated herein byreference, to the extent not inconsistent herewith.

While particular aspects of the present subject matter described hereinhave been shown and described, it will be apparent to those skilled inthe art that, based upon the teachings herein, changes and modificationsmay be made without departing from the subject matter described hereinand its broader aspects and, therefore, the appended claims are toencompass within their scope all such changes and modifications as arewithin the true spirit and scope of the subject matter described herein.It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.).

It will be further understood by those within the art that if a specificnumber of an introduced claim recitation is intended, such an intentwill be explicitly recited in the claim, and in the absence of suchrecitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to claims containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations).

Furthermore, in those instances where a convention analogous to “atleast one of A, B, and C, etc.” is used, in general such a constructionis intended in the sense one having skill in the art would understandthe convention (e.g., “a system having at least one of A, B, and C”would include but not be limited to systems that have A alone, B alone,C alone, A and B together, A and C together, B and C together, and/or A,B, and C together, etc.). In those instances where a conventionanalogous to “at least one of A, B, or C, etc.” is used, in general sucha construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, or C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that typically a disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms unless context dictates otherwise. For example, the phrase “Aor B” will be typically understood to include the possibilities of “A”or “B” or “A and B.”

With respect to the appended claims, those skilled in the art willappreciate that recited operations therein may generally be performed inany order. Also, although various operational flows are presented in asequence(s), it should be understood that the various operations may beperformed in other orders than those which are illustrated, or may beperformed concurrently. Examples of such alternate orderings may includeoverlapping, interleaved, interrupted, reordered, incremental,preparatory, supplemental, simultaneous, reverse, or other variantorderings, unless context dictates otherwise. Furthermore, terms like“responsive to,” “related to,” or other past-tense adjectives aregenerally not intended to exclude such variants, unless context dictatesotherwise.

This application may make reference to one or more trademarks, e.g., aword, letter, symbol, or device adopted by one manufacturer or merchantand used to identify and/or distinguish his or her product from those ofothers. Trademark names used herein are set forth in such language thatmakes clear their identity, that distinguishes them from commondescriptive nouns, that have fixed and definite meanings, or, in many ifnot all cases, are accompanied by other specific identification usingterms not covered by trademark. In addition, trademark names used hereinhave meanings that are well-known and defined in the literature, or donot refer to products or compounds for which knowledge of one or moretrade secrets is required in order to divine their meaning. Alltrademarks referenced in this application are the property of theirrespective owners, and the appearance of one or more trademarks in thisapplication does not diminish or otherwise adversely affect the validityof the one or more trademarks. All trademarks, registered orunregistered, that appear in this application are assumed to include aproper trademark symbol, e.g., the circle R or bracketed capitalization(e.g., [trademark name]), even when such trademark symbol does notexplicitly appear next to the trademark. To the extent a trademark isused in a descriptive manner to refer to a product or process, thattrademark should be interpreted to represent the corresponding productor process as of the date of the filing of this patent application.

Those skilled in the art will appreciate that the foregoing specificexemplary processes and/or devices and/or technologies arerepresentative of more general processes and/or devices and/ortechnologies taught elsewhere herein, such as in the claims filedherewith and/or elsewhere in the present application.

1. A computationally-implemented method, comprising: detecting speechdata related to a speech-facilitated transaction; acquiring adaptationdata that is at least partly based on at least one speech interaction ofa particular party that is discrete from the detected speech data,wherein at least a portion of the adaptation data has been stored on aparticular device associated with the particular party; obtaining adestination of one or more of the adaptation data and the speech data.transmitting one or more of the speech data and the adaptation data tothe acquired destination.
 2. (canceled)
 3. (canceled)
 4. (canceled) 5.(canceled)
 6. (canceled)
 7. (canceled)
 8. (canceled)
 9. Thecomputationally-implemented method of claim 1, wherein said detectingspeech data related to a speech-facilitated transaction comprises:receiving the adaptation data; and determining from the reception of theadaptation data that speech data related to a speech-facilitatedtransaction is being transferred.
 10. (canceled)
 11. Thecomputationally-implemented method of claim 1, wherein said detectingspeech data related to a speech-facilitated transaction comprises:receiving a signal requesting initiation of one or more operations inpreparation for a speech-facilitated transaction.
 12. (canceled) 13.(canceled)
 14. (canceled)
 15. (canceled)
 16. (canceled)
 17. (canceled)18. The computationally-implemented method of claim 1, wherein saiddetecting speech data related to a speech-facilitated transactioncomprises: detecting that a device is transmitting speech data; andcollecting data regarding the detected device that is transmittingspeech data.
 19. (canceled)
 20. (canceled)
 21. (canceled)
 22. (canceled)23. The computationally-implemented method of claim 1, wherein saiddetecting speech data related to a speech-facilitated transactioncomprises: receiving speech data comprising previously recorded speechby the particular party, and a timestamp corresponding to a time atwhich the speech data was recorded.
 24. (canceled)
 25. (canceled) 26.(canceled)
 27. (canceled)
 28. (canceled)
 29. (canceled)
 30. Thecomputationally-implemented method of claim 1, wherein said acquiringadaptation data that is at least partly based on at least one speechinteraction of a particular party that is discrete from the detectedspeech data, wherein at least a portion of the adaptation data has beenstored on a particular device associated with the particular partycomprises: acquiring data comprising one or more words and correspondingpronunciations of the one or more words that is at least partly based onat least one speech interaction of the particular party, said at leastone speech interaction of the particular party discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on the particular device associated with the particularparty.
 31. (canceled)
 32. (canceled)
 33. (canceled)
 34. (canceled) 35.The computationally-implemented method of claim 1, wherein saidacquiring adaptation data that is at least partly based on at least onespeech interaction of a particular party that is discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on a particular device associated with the particularparty comprises: acquiring adaptation data that is at least partly basedon at least one speech interaction of the particular party that occurredthat occurred at a different time and a different location than a speechinteraction prior to a speech interaction that generated the detectedspeech data, wherein at least a portion of the adaptation data has beenstored on the particular device associated with the particular party.36. The computationally-implemented method of claim 1, wherein saidacquiring adaptation data that is at least partly based on at least onespeech interaction of a particular party that is discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on a particular device associated with the particularparty comprises: acquiring at least a portion of adaptation data that isat least partly based on at least one speech interaction of theparticular party that occurred prior to a speech interaction thatgenerated the detected speech data, wherein at least a portion of theadaptation data has been stored on the particular device associated withthe particular party.
 37. (canceled)
 38. (canceled)
 39. Thecomputationally-implemented method of claim 36, wherein said acquiringat least a portion of adaptation data that is at least partly based onat least one speech interaction of the particular party that occurredprior to a speech interaction that generated the detected speech data,wherein at least a portion of the adaptation data has been stored on theparticular device associated with the particular party comprises:receiving, from a communication network provider, adaptation data thatis at least partly based on at least one speech interaction of theparticular party that occurred prior to a speech interaction thatgenerated the detected speech data, wherein at least a portion of theadaptation data has been stored on the particular device associated withthe particular party.
 40. The computationally-implemented method ofclaim 39, wherein said receiving, from a communication network provider,adaptation data that is at least partly based on at least one speechinteraction of the particular party that occurred prior to a speechinteraction that generated the detected speech data, wherein at least aportion of the adaptation data has been stored on the particular deviceassociated with the particular party comprises: receiving, from acommunication network provider, adaptation data that is at least partlybased on at least one speech interaction of the particular party thatoccurred prior to a speech interaction that generated the detectedspeech data, wherein at least a portion of the adaptation data has beenstored on the particular device associated with the particular party andpreviously transmitted to the communication network provider. 41.(canceled)
 42. (canceled)
 43. The computationally-implemented method ofclaim 1, wherein said acquiring adaptation data that is at least partlybased on at least one speech interaction of a particular party that isdiscrete from the detected speech data, wherein at least a portion ofthe adaptation data has been stored on a particular device associatedwith the particular party comprises: acquiring adaptation data inresponse to a detection of a particular condition, said adaptation dataat least partly based on at least one speech interaction of theparticular party that occurred prior to a speech interaction thatgenerated the detected speech data, wherein at least a portion of theadaptation data has been stored on the particular device associated withthe particular party.
 44. (canceled)
 45. (canceled)
 46. (canceled) 47.(canceled)
 48. (canceled)
 49. The computationally-implemented method ofclaim 1, wherein said acquiring adaptation data that is at least partlybased on at least one speech interaction of a particular party that isdiscrete from the detected speech data, wherein at least a portion ofthe adaptation data has been stored on a particular device associatedwith the particular party comprises: acquiring adaptation data from afurther device, said adaptation data at least partly based on at leastone speech interaction of the particular party that is discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on the particular device associated with the particularparty.
 50. The computationally-implemented method of claim 49, whereinsaid acquiring adaptation data from a further device, said adaptationdata at least partly based on at least one speech interaction of theparticular party that is discrete from the detected speech data, whereinat least a portion of the adaptation data has been stored on theparticular device associated with the particular party comprises:acquiring adaptation data from a further device, said adaptation dataoriginating at the further device and at least partly based on least onespeech interaction of the particular party that is discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on the particular device associated with the particularparty.
 51. (canceled)
 52. (canceled)
 53. (canceled)
 54. (canceled) 55.The computationally-implemented method of claim 49, wherein saidacquiring adaptation data from a further device, said adaptation data atleast partly based on at least one speech interaction of the particularparty that is discrete from the detected speech data, wherein at least aportion of the adaptation data has been stored on the particular deviceassociated with the particular party comprises: acquiring adaptationdata from a further device, said adaptation data received by the furtherdevice from the particular device, and said adaptation data at leastpartly based on least one speech interaction of the particular partythat is discrete from the detected speech data, wherein at least aportion of the adaptation data has been stored on the particular deviceassociated with the particular party.
 56. Thecomputationally-implemented method of claim 49, wherein said acquiringadaptation data from a further device, said adaptation data at leastpartly based on at least one speech interaction of the particular partythat is discrete from the detected speech data, wherein at least aportion of the adaptation data has been stored on the particular deviceassociated with the particular party comprises: acquiring adaptationdata, from a further device, said adaptation data comprisinginstructions for modifying a pronunciation dictionary, and saidadaptation data at least partly based on at least one speech interactionof the particular party that is discrete from the detected speech data,wherein at least a portion of the adaptation data has been stored on theparticular device associated with the particular party.
 57. (canceled)58. (canceled)
 59. (canceled)
 60. (canceled)
 61. Thecomputationally-implemented method of claim 1, wherein said acquiringadaptation data that is at least partly based on at least one speechinteraction of a particular party that is discrete from the detectedspeech data, wherein at least a portion of the adaptation data has beenstored on a particular device associated with the particular partycomprises: acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party with a particulartype of device, said at least one speech interaction discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on the particular device associated with the particularparty.
 62. The computationally-implemented method of claim 61, whereinsaid acquiring adaptation data that is at least partly based on at leastone speech interaction of the particular party with a particular type ofdevice, said at least one speech interaction discrete from the detectedspeech data, wherein at least a portion of the adaptation data has beenstored on the particular device associated with the particular partycomprises: acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party with the particulartype of device that is a same type of device as a target deviceconfigured to receive the speech data, said at least one speechinteraction discrete from the detected speech data, wherein at least aportion of the adaptation data has been stored on the particular deviceassociated with the particular party.
 63. (canceled)
 64. (canceled) 65.(canceled)
 66. (canceled)
 67. The computationally-implemented method ofclaim 1, wherein said acquiring adaptation data that is at least partlybased on at least one speech interaction of a particular party that isdiscrete from the detected speech data, wherein at least a portion ofthe adaptation data has been stored on a particular device associatedwith the particular party comprises: acquiring adaptation data that isat least partly based on at least one speech interaction of theparticular party with the particular device, said at least one speechinteraction is discrete from the detected speech data, wherein at leasta portion of the adaptation data has been stored on the particulardevice associated with the particular party.
 68. Thecomputationally-implemented method of claim 67, wherein said acquiringadaptation data that is at least partly based on at least one speechinteraction of the particular party with the particular device, said atleast one speech interaction is discrete from the detected speech data,wherein at least a portion of the adaptation data has been stored on theparticular device associated with the particular party comprises:acquiring adaptation data that is at least partly based on at least onespeech interaction of the particular party with a cellular telephonedevice, said at least one speech interaction is discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on the cellular telephone device associated with theparticular party.
 69. The computationally-implemented method of claim68, wherein said acquiring adaptation data that is at least partly basedon at least one speech interaction of the particular party with acellular telephone device, said at least one speech interaction isdiscrete from the detected speech data, wherein at least a portion ofthe adaptation data has been stored on the cellular telephone deviceassociated with the particular party comprises: acquiring adaptationdata that is at least partly based on at least one telephoneconversation carried out using the cellular telephone device, said atleast one telephone conversation is different than speech that is partof the detected speech data, wherein at least a portion of theadaptation data has been stored on the cellular telephone deviceassociated with the particular party.
 70. (canceled)
 71. Thecomputationally-implemented method of claim 1, wherein said acquiringadaptation data that is at least partly based on at least one speechinteraction of a particular party that is discrete from the detectedspeech data, wherein at least a portion of the adaptation data has beenstored on a particular device associated with the particular partycomprises: acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party that used one ormore same utterances as speech used in the detected speech data, saidone or more same utterances spoken to a different device than a targetdevice to which the detected speech data is directed.
 72. (canceled) 73.(canceled)
 74. (canceled)
 75. (canceled)
 76. (canceled)
 77. Thecomputationally-implemented method of claim 1, wherein said acquiringadaptation data that is at least partly based on at least one speechinteraction of a particular party that is discrete from the detectedspeech data, wherein at least a portion of the adaptation data has beenstored on a particular device associated with the particular partycomprises: acquiring a location of one or more instructions formodifying one or more portions of a speech recognition component of atarget device, said instructions at least partly based on at least onespeech interaction of the particular party that is discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on a particular device associated with the particularparty.
 78. (canceled)
 79. The computationally-implemented method ofclaim 1, wherein said acquiring adaptation data that is at least partlybased on at least one speech interaction of a particular party that isdiscrete from the detected speech data, wherein at least a portion ofthe adaptation data has been stored on a particular device associatedwith the particular party comprises: acquiring adaptation data that isat least partly based on at least one speech interaction of theparticular party that is discrete from the detected speech data, whereinat least a portion of the adaptation data is stored on the particulardevice associated with the particular party.
 80. Thecomputationally-implemented method of claim 1, wherein said acquiringadaptation data that is at least partly based on at least one speechinteraction of a particular party that is discrete from the detectedspeech data, wherein at least a portion of the adaptation data has beenstored on a particular device associated with the particular partycomprises: acquiring adaptation data that is at least partly based on atleast one speech interaction of the particular party that is discretefrom the detected speech data, wherein at least a portion of theadaptation data is temporarily stored on the particular deviceassociated with the particular party until it is deposited at a remoteserver.
 81. The computationally-implemented method of claim 1, whereinsaid acquiring adaptation data that is at least partly based on at leastone speech interaction of a particular party that is discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on a particular device associated with the particularparty comprises: acquiring adaptation data that is at least partly basedon at least one speech interaction of the particular party that isdiscrete from the detected speech data, wherein at least a portion ofthe adaptation data was transmitted from a first device to a seconddevice using the particular device associated with the particular partyas a conduit configured to facilitate the transmission.
 82. (canceled)83. The computationally-implemented method of claim 1, wherein saidacquiring adaptation data that is at least partly based on at least onespeech interaction of a particular party that is discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on a particular device associated with the particularparty comprises: acquiring adaptation data from a remote location, saidadaptation data at least partly based on at least one speech interactionof the particular party that is discrete from the detected speech data,wherein at least a portion of the adaptation data was transmitted to theremote location from the particular device associated with theparticular party.
 84. The computationally-implemented method of claim 1,wherein said acquiring adaptation data that is at least partly based onat least one speech interaction of a particular party that is discretefrom the detected speech data, wherein at least a portion of theadaptation data has been stored on a particular device associated withthe particular party comprises: receiving adaptation data that is atleast partly based on at least one speech interaction of the particularparty that is discrete from the detected speech data; and adding furtherdata to the received adaptation data.
 85. (canceled)
 86. Thecomputationally-implemented method of claim 84, wherein said addingfurther data to the received adaptation data comprises: adding headerdata identifying an entity that received the adaptation data. 87.(canceled)
 88. The computationally-implemented method of claim 1,wherein said obtaining a destination of one or more of the adaptationdata and the speech data comprises: receiving data regarding a targetdevice configured to process the speech data.
 89. Thecomputationally-implemented method of claim 88, wherein said receivingdata regarding a target device configured to process the speech datacomprises: receiving data comprising an address of a target deviceconfigured to process the speech data.
 90. (canceled)
 91. Thecomputationally-implemented method of claim 1, wherein said obtaining adestination of one or more of the adaptation data and the speech datacomprises: determining a location of a target device that is adestination of one or more of the adaptation data and the speech data.92. The computationally-implemented method of claim 91, wherein saiddetermining a location of a target device that is a destination of oneor more of the adaptation data and the speech data comprises:determining a location on a network of a target device that is adestination of one or more of the adaptation data and the speech data.93. (canceled)
 94. (canceled)
 95. The computationally-implemented methodof claim 1, wherein said obtaining a destination of one or more of theadaptation data and the speech data comprises: determining a programcomponent that is configured to perform processing on one or more of theadaptation data and the speech data.
 96. The computationally-implementedmethod of claim 1, wherein said obtaining a destination of one or moreof the adaptation data and the speech data comprises: determining aprogram component that is a destination of one or more of the adaptationdata and the speech data.
 97. The computationally-implemented method ofclaim 96, wherein said determining a program component that is adestination of one or more of the adaptation data and the speech datacomprises: selecting between an operating system component and anapplication component as a destination of one or more of the adaptationdata and the speech data.
 98. The computationally-implemented method ofclaim 1, wherein said obtaining a destination of one or more of theadaptation data and the speech data comprises: acquiring data regardingone or more other devices configured to process detected speech data;and determining a destination of the detected speech data based on thedata regarding one or more other devices configured to process detectedspeech data.
 99. The computationally-implemented method of claim 98,wherein said acquiring data regarding one or more other devicesconfigured to process detected speech data comprises: detecting one ormore other devices configured to process detected speech data. 100.(canceled)
 101. (canceled)
 102. (canceled)
 103. Thecomputationally-implemented method of claim 98, wherein said acquiringdata regarding one or more other devices configured to process detectedspeech data comprises: detecting one or more other devices configured toprocess detected speech data; transmitting a signal to the one or moreother devices requesting data regarding a capability of the one or moreother devices configured to process detected speech data; and receivingdata regarding the capability of the one or more other devicesconfigured to process detected speech data.
 104. Thecomputationally-implemented method of claim 98, wherein said acquiringdata regarding one or more other devices configured to process detectedspeech data comprises: detecting one or more other devices configured toprocess detected speech data; and receiving data regarding a capabilityof the detected one or more other devices configured to process detectedspeech data.
 105. The computationally-implemented method of claim 104,wherein said receiving data regarding a capability of the detected oneor more other devices configured to process detected speech datacomprises: receiving data regarding the capability of the one or moreother devices configured to process detected speech data from a furtherdevice that is not one of the one or more other devices.
 106. Thecomputationally-implemented method of claim 105, wherein said receivingdata regarding the capability of the one or more other devicesconfigured to process detected speech data from a further device that isnot one of the one or more other devices comprises: receiving dataregarding the capability of the one or more other devices configured toprocess detected speech data from a further device that is configured tocommunicate on a same network as the one or more other devices.
 107. Thecomputationally-implemented method of claim 105, wherein said receivingdata regarding the capability of the one or more other devicesconfigured to process detected speech data from a further device that isnot one of the one or more other devices comprises: receiving dataregarding the capability of the one or more other devices configured toprocess detected speech data from a further device that is at leastpartially controlled by a same entity that controls at least one of theone or more other devices.
 108. The computationally-implemented methodof claim 105, wherein said receiving data regarding the capability ofthe one or more other devices configured to process detected speech datafrom a further device that is not one of the one or more other devicescomprises: receiving data regarding the capability of the one or moreother devices configured to process detected speech data from a furtherdevice that is configured to provide one or more services to at leastone of the one or more other devices.
 109. Thecomputationally-implemented method of claim 1, wherein said obtaining adestination of one or more of the adaptation data and the speech datacomprises: acquiring other device data regarding a capability of one ormore other devices configured to process detected speech data; anddetermining a destination of the one or more of the adaptation data andthe speech data at least partly based on the acquired other device data.110. (canceled)
 111. (canceled)
 112. The computationally-implementedmethod of claim 109, wherein said acquiring other device data regardinga capability of one or more other devices configured to process detectedspeech data comprises: acquiring other device data regarding a speechdata processing capability of one or more detected other devices. 113.(canceled)
 114. (canceled)
 115. (canceled)
 116. (canceled)
 117. Thecomputationally-implemented method of claim 1, wherein said transmittingone or more of the speech data and the adaptation data to the acquireddestination comprises: transmitting one or more of the speech data andthe adaptation data to a target device acquired as the destination. 118.(canceled)
 119. The computationally-implemented method of claim 117,wherein said transmitting one or more of the speech data and theadaptation data to a target device acquired as the destinationcomprises: transmitting the detected speech data to the target deviceacquired as the destination.
 120. The computationally-implemented methodof claim 119, wherein said transmitting the detected speech data to thetarget device acquired as the destination comprises: converting detectedspeech data into data that is recognizable by the target device; andtransmitting the converted detected speech data to the target deviceacquired as the destination.
 121. The computationally-implemented methodof claim 120, wherein said converting detected speech data into datathat is recognizable by the target device comprises: converting detectedspeech data into data that is recognizable by the target device, whereinsaid detected speech data is unrecognizable to the target device priorto conversion.
 122. The computationally-implemented method of claim 120,wherein said converting detected speech data into data that isrecognizable by the target device comprises: converting detected speechdata into data that is recognizable by a target device, wherein thetarget device is configured to process the converted data more quicklythan the target device is configured to process speech data. 123.(canceled)
 124. The computationally-implemented method of claim 122,wherein said converting detected speech data into data that isrecognizable by a target device, wherein the target device is configuredto process the converted data more quickly than the target device isconfigured to process speech data comprises: converting detected speechdata into data that is recognizable by a target device, based onfeedback received from the target device indicating that the targetdevice is configured to process the converted data more quickly than thetarget device is configured to process speech data.
 125. (canceled) 126.(canceled)
 127. (canceled)
 128. The computationally-implemented methodof claim 1, wherein said transmitting one or more of the speech data andthe adaptation data to the acquired destination comprises: transmittingone or more of the speech data and the adaptation data to a particularcomponent of a target device.
 129. (canceled)
 130. (canceled) 131.(canceled)
 132. The computationally-implemented method of claim 1,wherein said transmitting one or more of the speech data and theadaptation data to the acquired destination comprises: transmitting oneor more of the speech data and the adaptation data to a further device,said one or more of the speech data and the adaptation data configuredto be processed by a target device. 133-266. (canceled)
 267. Acomputationally-implemented system, comprising circuitry for detectingspeech data related to a speech-facilitated transaction; circuitry foracquiring adaptation data that is at least partly based on at least onespeech interaction of a particular party that is discrete from thedetected speech data, wherein at least a portion of the adaptation datahas been stored on a particular device associated with the particularparty; circuitry for obtaining a destination of one or more of theadaptation data and the speech data; and circuitry for transmitting oneor more of the speech data and the adaptation data to the acquireddestination.
 268. (canceled)
 269. A device defined by a computationallanguage comprising: one or more interchained physical machines orderedfor detecting speech data related to a speech-facilitated transaction;one or more interchained physical machines ordered for acquiringadaptation data that is at least partly based on at least one speechinteraction of a particular party that is discrete from the detectedspeech data, wherein at least a portion of the adaptation data has beenstored on a particular device associated with the particular party; oneor more interchained physical machines ordered for obtaining adestination of one or more of the adaptation data and the speech data;and one or more interchained physical machines ordered for transmittingone or more of the speech data and the adaptation data to the acquireddestination.